Overview

Dataset statistics

Number of variables38
Number of observations500
Missing cells2825
Missing cells (%)14.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory148.6 KiB
Average record size in memory304.3 B

Variable types

Numeric8
Categorical30

Alerts

Power Source has constant value "Submitted" Constant
Memory has constant value "513,792" Constant
OS Family has constant value "Linux" Constant
Name has a high cardinality: 348 distinct values High cardinality
Computer has a high cardinality: 304 distinct values High cardinality
Site has a high cardinality: 221 distinct values High cardinality
Total Cores has a high cardinality: 345 distinct values High cardinality
Accelerator/Co-Processor Cores has a high cardinality: 110 distinct values High cardinality
Rmax [TFlop/s] has a high cardinality: 411 distinct values High cardinality
Rpeak [TFlop/s] has a high cardinality: 391 distinct values High cardinality
Nmax has a high cardinality: 369 distinct values High cardinality
HPCG [TFlop/s] has a high cardinality: 98 distinct values High cardinality
Power (kW) has a high cardinality: 172 distinct values High cardinality
Processor has a high cardinality: 109 distinct values High cardinality
System Model has a high cardinality: 131 distinct values High cardinality
System Family has a high cardinality: 51 distinct values High cardinality
Site ID has a high cardinality: 233 distinct values High cardinality
Rank is highly correlated with Previous Rank and 10 other fieldsHigh correlation
Previous Rank is highly correlated with Rank and 12 other fieldsHigh correlation
First Appearance is highly correlated with Manufacturer and 12 other fieldsHigh correlation
First Rank is highly correlated with Rank and 11 other fieldsHigh correlation
Year is highly correlated with First Appearance and 11 other fieldsHigh correlation
Energy Efficiency [GFlops/Watts] is highly correlated with Manufacturer and 9 other fieldsHigh correlation
Cores per Socket is highly correlated with Manufacturer and 11 other fieldsHigh correlation
System ID is highly correlated with Rank and 14 other fieldsHigh correlation
Manufacturer is highly correlated with Rank and 21 other fieldsHigh correlation
Country is highly correlated with Previous Rank and 14 other fieldsHigh correlation
Segment is highly correlated with Manufacturer and 12 other fieldsHigh correlation
Nhalf is highly correlated with Rank and 21 other fieldsHigh correlation
HPCG [TFlop/s] is highly correlated with Rank and 20 other fieldsHigh correlation
Architecture is highly correlated with Manufacturer and 10 other fieldsHigh correlation
Processor Technology is highly correlated with Rank and 19 other fieldsHigh correlation
Processor Speed (MHz) is highly correlated with First Appearance and 16 other fieldsHigh correlation
Operating System is highly correlated with Rank and 21 other fieldsHigh correlation
Accelerator/Co-Processor is highly correlated with First Appearance and 17 other fieldsHigh correlation
Processor Generation is highly correlated with Rank and 19 other fieldsHigh correlation
System Family is highly correlated with Rank and 21 other fieldsHigh correlation
Interconnect Family is highly correlated with Previous Rank and 16 other fieldsHigh correlation
Interconnect is highly correlated with Rank and 21 other fieldsHigh correlation
Continent is highly correlated with Manufacturer and 9 other fieldsHigh correlation
Previous Rank has 39 (7.8%) missing values Missing
Name has 141 (28.2%) missing values Missing
Accelerator/Co-Processor Cores has 330 (66.0%) missing values Missing
Nhalf has 487 (97.4%) missing values Missing
HPCG [TFlop/s] has 398 (79.6%) missing values Missing
Power (kW) has 309 (61.8%) missing values Missing
Power Source has 309 (61.8%) missing values Missing
Energy Efficiency [GFlops/Watts] has 309 (61.8%) missing values Missing
Memory has 499 (99.8%) missing values Missing
Rank is uniformly distributed Uniform
Previous Rank is uniformly distributed Uniform
Name is uniformly distributed Uniform
Nhalf is uniformly distributed Uniform
HPCG [TFlop/s] is uniformly distributed Uniform
Power (kW) is uniformly distributed Uniform
Rank has unique values Unique
System ID has unique values Unique

Reproduction

Analysis started2022-09-22 13:12:57.381093
Analysis finished2022-09-22 13:13:06.659580
Duration9.28 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Rank
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:06.721488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.4818328
Coefficient of variation (CV)0.5767737835
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2022-09-22T16:13:06.813171image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.2%
3301
 
0.2%
3431
 
0.2%
3421
 
0.2%
3411
 
0.2%
3401
 
0.2%
3391
 
0.2%
3381
 
0.2%
3371
 
0.2%
3361
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
5001
0.2%
4991
0.2%
4981
0.2%
4971
0.2%
4961
0.2%
4951
0.2%
4941
0.2%
4931
0.2%
4921
0.2%
4911
0.2%

Previous Rank
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
UNIFORM

Distinct461
Distinct (%)100.0%
Missing39
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean232.9023861
Minimum1
Maximum465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:06.905652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1117
median232
Q3349
95-th percentile442
Maximum465
Range464
Interquartile range (IQR)232

Descriptive statistics

Standard deviation134.3223783
Coefficient of variation (CV)0.5767325126
Kurtosis-1.201938305
Mean232.9023861
Median Absolute Deviation (MAD)116
Skewness0.001330421285
Sum107368
Variance18042.50132
MonotonicityNot monotonic
2022-09-22T16:13:06.990849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3201
 
0.2%
3181
 
0.2%
3171
 
0.2%
3161
 
0.2%
3151
 
0.2%
3141
 
0.2%
3131
 
0.2%
3121
 
0.2%
3111
 
0.2%
3101
 
0.2%
Other values (451)451
90.2%
(Missing)39
 
7.8%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
ValueCountFrequency (%)
4651
0.2%
4641
0.2%
4631
0.2%
4621
0.2%
4611
0.2%
4601
0.2%
4591
0.2%
4581
0.2%
4571
0.2%
4561
0.2%

First Appearance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.156
Minimum35
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:07.063216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile45
Q152
median55
Q357
95-th percentile59
Maximum59
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.938869557
Coefficient of variation (CV)0.07273191441
Kurtosis3.070949192
Mean54.156
Median Absolute Deviation (MAD)2
Skewness-1.443136624
Sum27078
Variance15.51469339
MonotonicityNot monotonic
2022-09-22T16:13:07.130100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5471
14.2%
5869
13.8%
5755
11.0%
5555
11.0%
5144
8.8%
5340
8.0%
5939
7.8%
5639
7.8%
5234
6.8%
4810
 
2.0%
Other values (13)44
8.8%
ValueCountFrequency (%)
351
 
0.2%
361
 
0.2%
371
 
0.2%
401
 
0.2%
412
 
0.4%
421
 
0.2%
434
 
0.8%
445
1.0%
4510
2.0%
461
 
0.2%
ValueCountFrequency (%)
5939
7.8%
5869
13.8%
5755
11.0%
5639
7.8%
5555
11.0%
5471
14.2%
5340
8.0%
5234
6.8%
5144
8.8%
502
 
0.4%

First Rank
Real number (ℝ≥0)

HIGH CORRELATION

Distinct250
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.524
Minimum1
Maximum423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:07.208603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median116.5
Q3176
95-th percentile303.05
Maximum423
Range422
Interquartile range (IQR)124

Descriptive statistics

Standard deviation90.80341372
Coefficient of variation (CV)0.7120496042
Kurtosis0.5375954009
Mean127.524
Median Absolute Deviation (MAD)62.5
Skewness0.8613734414
Sum63762
Variance8245.259944
MonotonicityNot monotonic
2022-09-22T16:13:07.458838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117
 
1.4%
16
 
1.2%
1146
 
1.2%
295
 
1.0%
1305
 
1.0%
195
 
1.0%
285
 
1.0%
125
 
1.0%
1354
 
0.8%
264
 
0.8%
Other values (240)448
89.6%
ValueCountFrequency (%)
16
1.2%
32
 
0.4%
53
0.6%
62
 
0.4%
74
0.8%
81
 
0.2%
91
 
0.2%
103
0.6%
117
1.4%
125
1.0%
ValueCountFrequency (%)
4231
0.2%
4141
0.2%
4032
0.4%
4011
0.2%
3981
0.2%
3931
0.2%
3791
0.2%
3721
0.2%
3632
0.4%
3621
0.2%

Name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct348
Distinct (%)96.9%
Missing141
Missing (%)28.2%
Memory size4.0 KiB
A14A
 
3
A1A
 
2
HKVDPSystem
 
2
Bank P A2
 
2
A8A
 
2
Other values (343)
348 

Length

Max length38
Median length27
Mean length9.47632312
Min length2

Characters and Unicode

Total characters3402
Distinct characters73
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)94.2%

Sample

1st rowFrontier
2nd rowSupercomputer Fugaku
3rd rowLUMI
4th rowSummit
5th rowSierra

Common Values

ValueCountFrequency (%)
A14A3
 
0.6%
A1A2
 
0.4%
HKVDPSystem2
 
0.4%
Bank P A22
 
0.4%
A8A2
 
0.4%
A7A2
 
0.4%
Jean Zay2
 
0.4%
bsystem2
 
0.4%
A13A2
 
0.4%
A17A2
 
0.4%
Other values (338)338
67.6%
(Missing)141
28.2%

Length

2022-09-22T16:13:07.548900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
company20
 
3.6%
m18
 
3.2%
software18
 
3.2%
gpu7
 
1.3%
cpu6
 
1.1%
module6
 
1.1%
cts-15
 
0.9%
5
 
0.9%
25
 
0.9%
partition5
 
0.9%
Other values (393)464
83.0%

Most occurring characters

ValueCountFrequency (%)
a230
 
6.8%
e214
 
6.3%
201
 
5.9%
r157
 
4.6%
A153
 
4.5%
o144
 
4.2%
t139
 
4.1%
n137
 
4.0%
i124
 
3.6%
S103
 
3.0%
Other values (63)1800
52.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1838
54.0%
Uppercase Letter1019
30.0%
Decimal Number203
 
6.0%
Space Separator201
 
5.9%
Dash Punctuation65
 
1.9%
Open Punctuation30
 
0.9%
Close Punctuation30
 
0.9%
Other Punctuation15
 
0.4%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a230
12.5%
e214
11.6%
r157
 
8.5%
o144
 
7.8%
t139
 
7.6%
n137
 
7.5%
i124
 
6.7%
s84
 
4.6%
u79
 
4.3%
l77
 
4.2%
Other values (19)453
24.6%
Uppercase Letter
ValueCountFrequency (%)
A153
15.0%
S103
 
10.1%
C92
 
9.0%
P63
 
6.2%
M58
 
5.7%
I55
 
5.4%
T51
 
5.0%
B38
 
3.7%
U37
 
3.6%
G36
 
3.5%
Other values (16)333
32.7%
Decimal Number
ValueCountFrequency (%)
183
40.9%
244
21.7%
023
 
11.3%
317
 
8.4%
411
 
5.4%
57
 
3.4%
76
 
3.0%
66
 
3.0%
84
 
2.0%
92
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/6
40.0%
.5
33.3%
,4
26.7%
Space Separator
ValueCountFrequency (%)
201
100.0%
Dash Punctuation
ValueCountFrequency (%)
-65
100.0%
Open Punctuation
ValueCountFrequency (%)
(30
100.0%
Close Punctuation
ValueCountFrequency (%)
)30
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2857
84.0%
Common545
 
16.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a230
 
8.1%
e214
 
7.5%
r157
 
5.5%
A153
 
5.4%
o144
 
5.0%
t139
 
4.9%
n137
 
4.8%
i124
 
4.3%
S103
 
3.6%
C92
 
3.2%
Other values (45)1364
47.7%
Common
ValueCountFrequency (%)
201
36.9%
183
15.2%
-65
 
11.9%
244
 
8.1%
(30
 
5.5%
)30
 
5.5%
023
 
4.2%
317
 
3.1%
411
 
2.0%
57
 
1.3%
Other values (8)34
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3398
99.9%
None4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a230
 
6.8%
e214
 
6.3%
201
 
5.9%
r157
 
4.6%
A153
 
4.5%
o144
 
4.2%
t139
 
4.1%
n137
 
4.0%
i124
 
3.6%
S103
 
3.0%
Other values (59)1796
52.9%
None
ValueCountFrequency (%)
é1
25.0%
ï1
25.0%
ã1
25.0%
ê1
25.0%

Computer
Categorical

HIGH CARDINALITY

Distinct304
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
ThinkSystem C0366, Xeon Gold 6252 24C 2.1GHz, 100G Ethernet
 
26
Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G Ethernet
 
19
Lenovo HR650x, Xeon Gold 6133 20C 2.5GHz, 25G Ethernet
 
16
ThinkSystem C2397, Xeon Platinum 8280 28C 2.7GHz, 100G Ethernet
 
14
ThinkSystem HR650X, Xeon Gold 6233 24C 2.5GHz, 25G Ethernet
 
11
Other values (299)
414 

Length

Max length120
Median length100.5
Mean length68.282
Min length47

Characters and Unicode

Total characters34141
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)48.2%

Sample

1st rowHPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-11
2nd rowSupercomputer Fugaku, A64FX 48C 2.2GHz, Tofu interconnect D
3rd rowHPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-11
4th rowIBM Power System AC922, IBM POWER9 22C 3.07GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR Infiniband
5th rowIBM Power System AC922, IBM POWER9 22C 3.1GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR Infiniband

Common Values

ValueCountFrequency (%)
ThinkSystem C0366, Xeon Gold 6252 24C 2.1GHz, 100G Ethernet26
 
5.2%
Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G Ethernet19
 
3.8%
Lenovo HR650x, Xeon Gold 6133 20C 2.5GHz, 25G Ethernet16
 
3.2%
ThinkSystem C2397, Xeon Platinum 8280 28C 2.7GHz, 100G Ethernet14
 
2.8%
ThinkSystem HR650X, Xeon Gold 6233 24C 2.5GHz, 25G Ethernet11
 
2.2%
Cray XC40, Xeon E5-2695v4 18C 2.1GHz, Aries interconnect 8
 
1.6%
ThinkSystem HR650X, Xeon Gold 6233 24C 2.5GHz, 10G Ethernet8
 
1.6%
HPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-117
 
1.4%
ThinkSystem SR590, Xeon Gold 5218 16C 2.3GHz, 10G Ethernet6
 
1.2%
Sugon TC6000, Xeon Gold 6130 16C 2.1GHz, 25G Ethernet6
 
1.2%
Other values (294)379
75.8%

Length

2022-09-22T16:13:07.648420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xeon396
 
7.5%
gold218
 
4.2%
ethernet203
 
3.9%
infiniband184
 
3.5%
nvidia172
 
3.3%
thinksystem122
 
2.3%
amd101
 
1.9%
24c100
 
1.9%
20c95
 
1.8%
epyc92
 
1.8%
Other values (459)3565
67.9%

Most occurring characters

ValueCountFrequency (%)
4788
 
14.0%
n1891
 
5.5%
e1525
 
4.5%
01519
 
4.4%
21439
 
4.2%
,1177
 
3.4%
G1037
 
3.0%
o991
 
2.9%
t927
 
2.7%
C872
 
2.6%
Other values (58)17975
52.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11812
34.6%
Uppercase Letter8383
24.6%
Decimal Number7224
21.2%
Space Separator4788
14.0%
Other Punctuation1703
 
5.0%
Dash Punctuation219
 
0.6%
Format10
 
< 0.1%
Connector Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1891
16.0%
e1525
12.9%
o991
 
8.4%
t927
 
7.8%
i825
 
7.0%
l818
 
6.9%
a695
 
5.9%
r522
 
4.4%
z506
 
4.3%
d471
 
4.0%
Other values (16)2641
22.4%
Uppercase Letter
ValueCountFrequency (%)
G1037
12.4%
C872
10.4%
H752
 
9.0%
I725
 
8.6%
X619
 
7.4%
E589
 
7.0%
D540
 
6.4%
A438
 
5.2%
S417
 
5.0%
P375
 
4.5%
Other values (15)2019
24.1%
Decimal Number
ValueCountFrequency (%)
01519
21.0%
21439
19.9%
1865
12.0%
6820
11.4%
4681
9.4%
5666
9.2%
8437
 
6.0%
3421
 
5.8%
7267
 
3.7%
9109
 
1.5%
Other Punctuation
ValueCountFrequency (%)
,1177
69.1%
.465
 
27.3%
/61
 
3.6%
Space Separator
ValueCountFrequency (%)
4788
100.0%
Dash Punctuation
ValueCountFrequency (%)
-219
100.0%
Format
ValueCountFrequency (%)
10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin20195
59.2%
Common13946
40.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1891
 
9.4%
e1525
 
7.6%
G1037
 
5.1%
o991
 
4.9%
t927
 
4.6%
C872
 
4.3%
i825
 
4.1%
l818
 
4.1%
H752
 
3.7%
I725
 
3.6%
Other values (41)9832
48.7%
Common
ValueCountFrequency (%)
4788
34.3%
01519
 
10.9%
21439
 
10.3%
,1177
 
8.4%
1865
 
6.2%
6820
 
5.9%
4681
 
4.9%
5666
 
4.8%
.465
 
3.3%
8437
 
3.1%
Other values (7)1089
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII34131
> 99.9%
Punctuation10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4788
 
14.0%
n1891
 
5.5%
e1525
 
4.5%
01519
 
4.5%
21439
 
4.2%
,1177
 
3.4%
G1037
 
3.0%
o991
 
2.9%
t927
 
2.7%
C872
 
2.6%
Other values (57)17965
52.6%
Punctuation
ValueCountFrequency (%)
10
100.0%

Site
Categorical

HIGH CARDINALITY

Distinct221
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Service Provider T
58 
Software Company MUS
 
26
Internet Company
 
18
Hosting Services
 
14
Government
 
14
Other values (216)
370 

Length

Max length116
Median length70
Mean length24.268
Min length3

Characters and Unicode

Total characters12134
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)26.4%

Sample

1st rowDOE/SC/Oak Ridge National Laboratory
2nd rowRIKEN Center for Computational Science
3rd rowEuroHPC/CSC
4th rowDOE/SC/Oak Ridge National Laboratory
5th rowDOE/NNSA/LLNL

Common Values

ValueCountFrequency (%)
Service Provider T58
 
11.6%
Software Company MUS26
 
5.2%
Internet Company18
 
3.6%
Hosting Services14
 
2.8%
Government14
 
2.8%
DJIT Company12
 
2.4%
Internet Service (B)7
 
1.4%
Internet Service P6
 
1.2%
NVIDIA Corporation6
 
1.2%
Saudi Aramco5
 
1.0%
Other values (211)334
66.8%

Length

2022-09-22T16:13:07.756728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
company89
 
5.6%
service86
 
5.4%
provider68
 
4.3%
t58
 
3.7%
national44
 
2.8%
internet42
 
2.6%
software41
 
2.6%
center39
 
2.5%
university37
 
2.3%
of32
 
2.0%
Other values (383)1051
66.2%

Most occurring characters

ValueCountFrequency (%)
e1155
 
9.5%
1091
 
9.0%
r850
 
7.0%
n805
 
6.6%
o777
 
6.4%
i695
 
5.7%
t678
 
5.6%
a675
 
5.6%
c366
 
3.0%
S360
 
3.0%
Other values (65)4682
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8544
70.4%
Uppercase Letter2212
 
18.2%
Space Separator1091
 
9.0%
Other Punctuation120
 
1.0%
Open Punctuation61
 
0.5%
Close Punctuation61
 
0.5%
Dash Punctuation34
 
0.3%
Decimal Number10
 
0.1%
Math Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1155
13.5%
r850
9.9%
n805
9.4%
o777
 
9.1%
i695
 
8.1%
t678
 
7.9%
a675
 
7.9%
c366
 
4.3%
s286
 
3.3%
v271
 
3.2%
Other values (24)1986
23.2%
Uppercase Letter
ValueCountFrequency (%)
S360
16.3%
C316
14.3%
I174
 
7.9%
T162
 
7.3%
A142
 
6.4%
N122
 
5.5%
P122
 
5.5%
U99
 
4.5%
E90
 
4.1%
M87
 
3.9%
Other values (17)538
24.3%
Other Punctuation
ValueCountFrequency (%)
/72
60.0%
,19
 
15.8%
.17
 
14.2%
'8
 
6.7%
&3
 
2.5%
#1
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
-33
97.1%
1
 
2.9%
Decimal Number
ValueCountFrequency (%)
26
60.0%
44
40.0%
Space Separator
ValueCountFrequency (%)
1091
100.0%
Open Punctuation
ValueCountFrequency (%)
(61
100.0%
Close Punctuation
ValueCountFrequency (%)
)61
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10756
88.6%
Common1378
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1155
 
10.7%
r850
 
7.9%
n805
 
7.5%
o777
 
7.2%
i695
 
6.5%
t678
 
6.3%
a675
 
6.3%
c366
 
3.4%
S360
 
3.3%
C316
 
2.9%
Other values (51)4079
37.9%
Common
ValueCountFrequency (%)
1091
79.2%
/72
 
5.2%
(61
 
4.4%
)61
 
4.4%
-33
 
2.4%
,19
 
1.4%
.17
 
1.2%
'8
 
0.6%
26
 
0.4%
44
 
0.3%
Other values (4)6
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12117
99.9%
None16
 
0.1%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1155
 
9.5%
1091
 
9.0%
r850
 
7.0%
n805
 
6.6%
o777
 
6.4%
i695
 
5.7%
t678
 
5.6%
a675
 
5.6%
c366
 
3.0%
S360
 
3.0%
Other values (55)4665
38.5%
None
ValueCountFrequency (%)
ó4
25.0%
ö2
12.5%
ä2
12.5%
é2
12.5%
ü2
12.5%
ç1
 
6.2%
ã1
 
6.2%
í1
 
6.2%
Ü1
 
6.2%
Punctuation
ValueCountFrequency (%)
1
100.0%

Manufacturer
Categorical

HIGH CORRELATION

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Lenovo
161 
HPE
96 
Inspur
50 
Atos
42 
Sugon
36 
Other values (31)
115 

Length

Max length51
Median length31
Mean length5.924
Min length3

Characters and Unicode

Total characters2962
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.8%

Sample

1st rowHPE
2nd rowFujitsu
3rd rowHPE
4th rowIBM
5th rowIBM / NVIDIA / Mellanox

Common Values

ValueCountFrequency (%)
Lenovo161
32.2%
HPE96
19.2%
Inspur50
 
10.0%
Atos42
 
8.4%
Sugon36
 
7.2%
DELL EMC17
 
3.4%
Nvidia14
 
2.8%
Fujitsu13
 
2.6%
NEC10
 
2.0%
Huawei7
 
1.4%
Other values (26)54
 
10.8%

Length

2022-09-22T16:13:07.845219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lenovo162
28.5%
hpe96
16.9%
inspur51
 
9.0%
atos42
 
7.4%
sugon36
 
6.3%
nvidia19
 
3.3%
dell17
 
3.0%
emc17
 
3.0%
fujitsu14
 
2.5%
11
 
1.9%
Other values (41)103
18.1%

Most occurring characters

ValueCountFrequency (%)
o435
14.7%
n282
 
9.5%
e204
 
6.9%
L199
 
6.7%
v178
 
6.0%
E157
 
5.3%
u148
 
5.0%
s118
 
4.0%
H106
 
3.6%
P106
 
3.6%
Other values (45)1029
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1883
63.6%
Uppercase Letter988
33.4%
Space Separator68
 
2.3%
Other Punctuation21
 
0.7%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o435
23.1%
n282
15.0%
e204
10.8%
v178
9.5%
u148
 
7.9%
s118
 
6.3%
i84
 
4.5%
t83
 
4.4%
r79
 
4.2%
p62
 
3.3%
Other values (16)210
11.2%
Uppercase Letter
ValueCountFrequency (%)
L199
20.1%
E157
15.9%
H106
10.7%
P106
10.7%
I78
 
7.9%
A67
 
6.8%
C47
 
4.8%
S43
 
4.4%
N42
 
4.3%
M39
 
3.9%
Other values (14)104
10.5%
Other Punctuation
ValueCountFrequency (%)
/15
71.4%
.3
 
14.3%
,3
 
14.3%
Space Separator
ValueCountFrequency (%)
68
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2871
96.9%
Common91
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o435
15.2%
n282
 
9.8%
e204
 
7.1%
L199
 
6.9%
v178
 
6.2%
E157
 
5.5%
u148
 
5.2%
s118
 
4.1%
H106
 
3.7%
P106
 
3.7%
Other values (40)938
32.7%
Common
ValueCountFrequency (%)
68
74.7%
/15
 
16.5%
.3
 
3.3%
,3
 
3.3%
-2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o435
14.7%
n282
 
9.5%
e204
 
6.9%
L199
 
6.7%
v178
 
6.0%
E157
 
5.3%
u148
 
5.0%
s118
 
4.0%
H106
 
3.6%
P106
 
3.6%
Other values (45)1029
34.7%

Country
Categorical

HIGH CORRELATION

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
China
173 
United States
128 
Japan
33 
Germany
31 
France
22 
Other values (27)
113 

Length

Max length20
Median length14
Mean length8.004
Min length5

Characters and Unicode

Total characters4002
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.8%

Sample

1st rowUnited States
2nd rowJapan
3rd rowFinland
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
China173
34.6%
United States128
25.6%
Japan33
 
6.6%
Germany31
 
6.2%
France22
 
4.4%
Canada14
 
2.8%
United Kingdom12
 
2.4%
Russia7
 
1.4%
Netherlands6
 
1.2%
Brazil6
 
1.2%
Other values (22)68
 
13.6%

Length

2022-09-22T16:13:07.921937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
china173
26.4%
united142
21.6%
states128
19.5%
japan33
 
5.0%
germany31
 
4.7%
france22
 
3.4%
canada14
 
2.1%
kingdom12
 
1.8%
russia7
 
1.1%
netherlands6
 
0.9%
Other values (26)88
13.4%

Most occurring characters

ValueCountFrequency (%)
a563
14.1%
n470
11.7%
t429
10.7%
i383
9.6%
e369
9.2%
d204
 
5.1%
C189
 
4.7%
h187
 
4.7%
s157
 
3.9%
156
 
3.9%
Other values (31)895
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3190
79.7%
Uppercase Letter656
 
16.4%
Space Separator156
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a563
17.6%
n470
14.7%
t429
13.4%
i383
12.0%
e369
11.6%
d204
 
6.4%
h187
 
5.9%
s157
 
4.9%
r105
 
3.3%
m47
 
1.5%
Other values (12)276
8.7%
Uppercase Letter
ValueCountFrequency (%)
C189
28.8%
S155
23.6%
U142
21.6%
J33
 
5.0%
G31
 
4.7%
F26
 
4.0%
K18
 
2.7%
A15
 
2.3%
I12
 
1.8%
N8
 
1.2%
Other values (8)27
 
4.1%
Space Separator
ValueCountFrequency (%)
156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3846
96.1%
Common156
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a563
14.6%
n470
12.2%
t429
11.2%
i383
10.0%
e369
9.6%
d204
 
5.3%
C189
 
4.9%
h187
 
4.9%
s157
 
4.1%
S155
 
4.0%
Other values (30)740
19.2%
Common
ValueCountFrequency (%)
156
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4002
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a563
14.1%
n470
11.7%
t429
10.7%
i383
9.6%
e369
9.2%
d204
 
5.1%
C189
 
4.7%
h187
 
4.7%
s157
 
3.9%
156
 
3.9%
Other values (31)895
22.4%

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.308
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:07.988115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2016
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.835176216
Coefficient of variation (CV)0.0009088144136
Kurtosis2.218661745
Mean2019.308
Median Absolute Deviation (MAD)1
Skewness-1.124243696
Sum1009654
Variance3.367871743
MonotonicityNot monotonic
2022-09-22T16:13:08.053068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2021119
23.8%
2019107
21.4%
2020101
20.2%
201876
15.2%
202232
 
6.4%
201729
 
5.8%
201616
 
3.2%
201510
 
2.0%
20146
 
1.2%
20132
 
0.4%
Other values (2)2
 
0.4%
ValueCountFrequency (%)
20101
 
0.2%
20111
 
0.2%
20132
 
0.4%
20146
 
1.2%
201510
 
2.0%
201616
 
3.2%
201729
 
5.8%
201876
15.2%
2019107
21.4%
2020101
20.2%
ValueCountFrequency (%)
202232
 
6.4%
2021119
23.8%
2020101
20.2%
2019107
21.4%
201876
15.2%
201729
 
5.8%
201616
 
3.2%
201510
 
2.0%
20146
 
1.2%
20132
 
0.4%

Segment
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Industry
235 
Research
116 
Academic
86 
Government
33 
Vendor
 
17

Length

Max length10
Median length8
Mean length8.012
Min length6

Characters and Unicode

Total characters4006
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResearch
2nd rowResearch
3rd rowResearch
4th rowResearch
5th rowResearch

Common Values

ValueCountFrequency (%)
Industry235
47.0%
Research116
23.2%
Academic86
 
17.2%
Government33
 
6.6%
Vendor17
 
3.4%
Others13
 
2.6%

Length

2022-09-22T16:13:08.123755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:08.206669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
industry235
47.0%
research116
23.2%
academic86
 
17.2%
government33
 
6.6%
vendor17
 
3.4%
others13
 
2.6%

Most occurring characters

ValueCountFrequency (%)
r414
10.3%
e414
10.3%
s364
9.1%
d338
 
8.4%
n318
 
7.9%
c288
 
7.2%
t281
 
7.0%
I235
 
5.9%
u235
 
5.9%
y235
 
5.9%
Other values (11)884
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3506
87.5%
Uppercase Letter500
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r414
11.8%
e414
11.8%
s364
10.4%
d338
9.6%
n318
9.1%
c288
8.2%
t281
8.0%
u235
6.7%
y235
6.7%
a202
5.8%
Other values (5)417
11.9%
Uppercase Letter
ValueCountFrequency (%)
I235
47.0%
R116
23.2%
A86
 
17.2%
G33
 
6.6%
V17
 
3.4%
O13
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin4006
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r414
10.3%
e414
10.3%
s364
9.1%
d338
 
8.4%
n318
 
7.9%
c288
 
7.2%
t281
 
7.0%
I235
 
5.9%
u235
 
5.9%
y235
 
5.9%
Other values (11)884
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r414
10.3%
e414
10.3%
s364
9.1%
d338
 
8.4%
n318
 
7.9%
c288
 
7.2%
t281
 
7.0%
I235
 
5.9%
u235
 
5.9%
y235
 
5.9%
Other values (11)884
22.1%

Total Cores
Categorical

HIGH CARDINALITY

Distinct345
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
57,600
 
21
53,760
 
11
69,120
 
8
61,440
 
6
92,160
 
6
Other values (340)
448 

Length

Max length10
Median length6
Mean length6.304
Min length5

Characters and Unicode

Total characters3152
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique274 ?
Unique (%)54.8%

Sample

1st row8,730,112
2nd row7,630,848
3rd row1,110,144
4th row2,414,592
5th row1,572,480

Common Values

ValueCountFrequency (%)
57,60021
 
4.2%
53,76011
 
2.2%
69,1208
 
1.6%
61,4406
 
1.2%
92,1606
 
1.2%
48,1605
 
1.0%
50,4005
 
1.0%
38,4005
 
1.0%
84,4805
 
1.0%
76,8004
 
0.8%
Other values (335)424
84.8%

Length

2022-09-22T16:13:08.272847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
57,60021
 
4.2%
53,76011
 
2.2%
69,1208
 
1.6%
61,4406
 
1.2%
92,1606
 
1.2%
48,1605
 
1.0%
50,4005
 
1.0%
38,4005
 
1.0%
84,4805
 
1.0%
80,6404
 
0.8%
Other values (335)424
84.8%

Most occurring characters

ValueCountFrequency (%)
0633
20.1%
,509
16.1%
4306
9.7%
6283
9.0%
2267
8.5%
8239
 
7.6%
1226
 
7.2%
5189
 
6.0%
7180
 
5.7%
3174
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2643
83.9%
Other Punctuation509
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0633
24.0%
4306
11.6%
6283
10.7%
2267
10.1%
8239
 
9.0%
1226
 
8.6%
5189
 
7.2%
7180
 
6.8%
3174
 
6.6%
9146
 
5.5%
Other Punctuation
ValueCountFrequency (%)
,509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3152
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0633
20.1%
,509
16.1%
4306
9.7%
6283
9.0%
2267
8.5%
8239
 
7.6%
1226
 
7.2%
5189
 
6.0%
7180
 
5.7%
3174
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0633
20.1%
,509
16.1%
4306
9.7%
6283
9.0%
2267
8.5%
8239
 
7.6%
1226
 
7.2%
5189
 
6.0%
7180
 
5.7%
3174
 
5.5%

Accelerator/Co-Processor Cores
Categorical

HIGH CARDINALITY
MISSING

Distinct110
Distinct (%)64.7%
Missing330
Missing (%)66.0%
Memory size4.0 KiB
44,800
 
11
41,600
 
9
38,400
 
8
51,200
 
5
48,000
 
5
Other values (105)
132 

Length

Max length9
Median length6
Mean length6.264705882
Min length3

Characters and Unicode

Total characters1065
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)52.4%

Sample

1st row8,138,240
2nd row1,034,880
3rd row2,211,840
4th row1,382,400
5th row663,552

Common Values

ValueCountFrequency (%)
44,80011
 
2.2%
41,6009
 
1.8%
38,4008
 
1.6%
51,2005
 
1.0%
48,0005
 
1.0%
17,2805
 
1.0%
20,7364
 
0.8%
141,6964
 
0.8%
27,6484
 
0.8%
21,6003
 
0.6%
Other values (100)112
 
22.4%
(Missing)330
66.0%

Length

2022-09-22T16:13:08.338706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
44,80011
 
6.5%
41,6009
 
5.3%
38,4008
 
4.7%
51,2005
 
2.9%
48,0005
 
2.9%
17,2805
 
2.9%
20,7364
 
2.4%
141,6964
 
2.4%
27,6484
 
2.4%
21,6003
 
1.8%
Other values (100)112
65.9%

Most occurring characters

ValueCountFrequency (%)
0225
21.1%
,175
16.4%
4122
11.5%
299
9.3%
897
9.1%
184
 
7.9%
684
 
7.9%
358
 
5.4%
548
 
4.5%
742
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number890
83.6%
Other Punctuation175
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0225
25.3%
4122
13.7%
299
11.1%
897
10.9%
184
 
9.4%
684
 
9.4%
358
 
6.5%
548
 
5.4%
742
 
4.7%
931
 
3.5%
Other Punctuation
ValueCountFrequency (%)
,175
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1065
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0225
21.1%
,175
16.4%
4122
11.5%
299
9.3%
897
9.1%
184
 
7.9%
684
 
7.9%
358
 
5.4%
548
 
4.5%
742
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0225
21.1%
,175
16.4%
4122
11.5%
299
9.3%
897
9.1%
184
 
7.9%
684
 
7.9%
358
 
5.4%
548
 
4.5%
742
 
3.9%

Rmax [TFlop/s]
Categorical

HIGH CARDINALITY

Distinct411
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1,649.11
 
19
2,348.44
 
7
2,363.77
 
6
2,087.50
 
5
16,590.00
 
4
Other values (406)
459 

Length

Max length12
Median length8
Mean length8.112
Min length8

Characters and Unicode

Total characters4056
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique368 ?
Unique (%)73.6%

Sample

1st row1,102,000.00
2nd row442,010.00
3rd row151,900.00
4th row148,600.00
5th row94,640.00

Common Values

ValueCountFrequency (%)
1,649.1119
 
3.8%
2,348.447
 
1.4%
2,363.776
 
1.2%
2,087.505
 
1.0%
16,590.004
 
0.8%
2,870.314
 
0.8%
3,131.254
 
0.8%
2,121.004
 
0.8%
2,282.004
 
0.8%
2,329.003
 
0.6%
Other values (401)440
88.0%

Length

2022-09-22T16:13:08.405248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,649.1119
 
3.8%
2,348.447
 
1.4%
2,363.776
 
1.2%
2,087.505
 
1.0%
16,590.004
 
0.8%
2,870.314
 
0.8%
3,131.254
 
0.8%
2,121.004
 
0.8%
2,282.004
 
0.8%
2,609.383
 
0.6%
Other values (401)440
88.0%

Most occurring characters

ValueCountFrequency (%)
0668
16.5%
,501
12.4%
.500
12.3%
2411
10.1%
1384
9.5%
8250
 
6.2%
6241
 
5.9%
3237
 
5.8%
4231
 
5.7%
9227
 
5.6%
Other values (2)406
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3055
75.3%
Other Punctuation1001
 
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0668
21.9%
2411
13.5%
1384
12.6%
8250
 
8.2%
6241
 
7.9%
3237
 
7.8%
4231
 
7.6%
9227
 
7.4%
5205
 
6.7%
7201
 
6.6%
Other Punctuation
ValueCountFrequency (%)
,501
50.0%
.500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common4056
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0668
16.5%
,501
12.4%
.500
12.3%
2411
10.1%
1384
9.5%
8250
 
6.2%
6241
 
5.9%
3237
 
5.8%
4231
 
5.7%
9227
 
5.6%
Other values (2)406
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0668
16.5%
,501
12.4%
.500
12.3%
2411
10.1%
1384
9.5%
8250
 
6.2%
6241
 
5.9%
3237
 
5.8%
4231
 
5.7%
9227
 
5.6%
Other values (2)406
10.0%

Rpeak [TFlop/s]
Categorical

HIGH CARDINALITY

Distinct391
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2,119.68
 
19
4,644.86
 
13
6,193.15
 
7
4,368.00
 
6
4,128.77
 
5
Other values (386)
450 

Length

Max length12
Median length8
Mean length8.182
Min length8

Characters and Unicode

Total characters4091
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique341 ?
Unique (%)68.2%

Sample

1st row1,685,651.46
2nd row537,212.00
3rd row214,351.87
4th row200,794.88
5th row125,712.00

Common Values

ValueCountFrequency (%)
2,119.6819
 
3.8%
4,644.8613
 
2.6%
6,193.157
 
1.4%
4,368.006
 
1.2%
4,128.775
 
1.0%
5,677.064
 
0.8%
4,480.004
 
0.8%
24,557.234
 
0.8%
3,072.004
 
0.8%
4,056.004
 
0.8%
Other values (381)430
86.0%

Length

2022-09-22T16:13:08.477070image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,119.6819
 
3.8%
4,644.8613
 
2.6%
6,193.157
 
1.4%
4,368.006
 
1.2%
4,128.775
 
1.0%
5,677.064
 
0.8%
4,480.004
 
0.8%
24,557.234
 
0.8%
3,072.004
 
0.8%
4,056.004
 
0.8%
Other values (381)430
86.0%

Most occurring characters

ValueCountFrequency (%)
,501
12.2%
.500
12.2%
0465
11.4%
4371
9.1%
1329
8.0%
6326
8.0%
2310
7.6%
3300
7.3%
8293
7.2%
5254
6.2%
Other values (2)442
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3090
75.5%
Other Punctuation1001
 
24.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0465
15.0%
4371
12.0%
1329
10.6%
6326
10.6%
2310
10.0%
3300
9.7%
8293
9.5%
5254
8.2%
7223
7.2%
9219
7.1%
Other Punctuation
ValueCountFrequency (%)
,501
50.0%
.500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common4091
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,501
12.2%
.500
12.2%
0465
11.4%
4371
9.1%
1329
8.0%
6326
8.0%
2310
7.6%
3300
7.3%
8293
7.2%
5254
6.2%
Other values (2)442
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4091
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,501
12.2%
.500
12.2%
0465
11.4%
4371
9.1%
1329
8.0%
6326
8.0%
2310
7.6%
3300
7.3%
8293
7.2%
5254
6.2%
Other values (2)442
10.8%

Nmax
Categorical

HIGH CARDINALITY

Distinct369
Distinct (%)74.4%
Missing4
Missing (%)0.8%
Memory size4.0 KiB
4,957,440
 
28
8,586,432
 
19
4,293,120
 
15
1,387,000
 
11
1,284,000
 
10
Other values (364)
413 

Length

Max length10
Median length9
Mean length8.963709677
Min length7

Characters and Unicode

Total characters4446
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)66.5%

Sample

1st row24,440,832
2nd row21,288,960
3rd row8,709,120
4th row16,473,600
5th row11,902,464

Common Values

ValueCountFrequency (%)
4,957,44028
 
5.6%
8,586,43219
 
3.8%
4,293,12015
 
3.0%
1,387,00011
 
2.2%
1,284,00010
 
2.0%
1,436,0005
 
1.0%
1,337,0005
 
1.0%
1,483,0004
 
0.8%
2,621,3764
 
0.8%
4,800,0003
 
0.6%
Other values (359)392
78.4%
(Missing)4
 
0.8%

Length

2022-09-22T16:13:08.545968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,957,44028
 
5.6%
8,586,43219
 
3.8%
4,293,12015
 
3.0%
1,387,00011
 
2.2%
1,284,00010
 
2.0%
1,436,0005
 
1.0%
1,337,0005
 
1.0%
1,483,0004
 
0.8%
2,621,3764
 
0.8%
5,542,6563
 
0.6%
Other values (359)392
79.0%

Most occurring characters

ValueCountFrequency (%)
,978
22.0%
0702
15.8%
4459
10.3%
2356
 
8.0%
5322
 
7.2%
6304
 
6.8%
8297
 
6.7%
1294
 
6.6%
3264
 
5.9%
7244
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3468
78.0%
Other Punctuation978
 
22.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0702
20.2%
4459
13.2%
2356
10.3%
5322
9.3%
6304
8.8%
8297
8.6%
1294
8.5%
3264
 
7.6%
7244
 
7.0%
9226
 
6.5%
Other Punctuation
ValueCountFrequency (%)
,978
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4446
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
,978
22.0%
0702
15.8%
4459
10.3%
2356
 
8.0%
5322
 
7.2%
6304
 
6.8%
8297
 
6.7%
1294
 
6.6%
3264
 
5.9%
7244
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
,978
22.0%
0702
15.8%
4459
10.3%
2356
 
8.0%
5322
 
7.2%
6304
 
6.8%
8297
 
6.7%
1294
 
6.6%
3264
 
5.9%
7244
 
5.5%

Nhalf
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct13
Distinct (%)100.0%
Missing487
Missing (%)97.4%
Memory size4.0 KiB
1,169,680
5,184,000
192
2,500,000
1,000,000
Other values (8)

Length

Max length9
Median length7
Mean length7.076923077
Min length2

Characters and Unicode

Total characters92
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row1,169,680
2nd row5,184,000
3rd row192
4th row2,500,000
5th row1,000,000

Common Values

ValueCountFrequency (%)
1,169,6801
 
0.2%
5,184,0001
 
0.2%
1921
 
0.2%
2,500,0001
 
0.2%
1,000,0001
 
0.2%
-11
 
0.2%
668,5001
 
0.2%
725,7601
 
0.2%
700,0001
 
0.2%
790,2721
 
0.2%
Other values (3)3
 
0.6%
(Missing)487
97.4%

Length

2022-09-22T16:13:08.611071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,169,6801
 
7.7%
5,184,0001
 
7.7%
1921
 
7.7%
2,500,0001
 
7.7%
1,000,0001
 
7.7%
11
 
7.7%
668,5001
 
7.7%
725,7601
 
7.7%
700,0001
 
7.7%
790,2721
 
7.7%
Other values (3)3
23.1%

Most occurring characters

ValueCountFrequency (%)
029
31.5%
,16
17.4%
19
 
9.8%
77
 
7.6%
86
 
6.5%
26
 
6.5%
65
 
5.4%
95
 
5.4%
55
 
5.4%
42
 
2.2%
Other values (2)2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75
81.5%
Other Punctuation16
 
17.4%
Dash Punctuation1
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029
38.7%
19
 
12.0%
77
 
9.3%
86
 
8.0%
26
 
8.0%
65
 
6.7%
95
 
6.7%
55
 
6.7%
42
 
2.7%
31
 
1.3%
Other Punctuation
ValueCountFrequency (%)
,16
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common92
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029
31.5%
,16
17.4%
19
 
9.8%
77
 
7.6%
86
 
6.5%
26
 
6.5%
65
 
5.4%
95
 
5.4%
55
 
5.4%
42
 
2.2%
Other values (2)2
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII92
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029
31.5%
,16
17.4%
19
 
9.8%
77
 
7.6%
86
 
6.5%
26
 
6.5%
65
 
5.4%
95
 
5.4%
55
 
5.4%
42
 
2.2%
Other values (2)2
 
2.2%

HPCG [TFlop/s]
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Distinct98
Distinct (%)96.1%
Missing398
Missing (%)79.6%
Memory size4.0 KiB
455.56
 
4
65.46
 
2
48.60
 
1
52.57
 
1
43.67
 
1
Other values (93)
93 

Length

Max length9
Median length8
Mean length5.666666667
Min length5

Characters and Unicode

Total characters578
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)94.1%

Sample

1st row16,004.50
2nd row1,935.73
3rd row2,925.75
4th row1,795.67
5th row480.85

Common Values

ValueCountFrequency (%)
455.564
 
0.8%
65.462
 
0.4%
48.601
 
0.2%
52.571
 
0.2%
43.671
 
0.2%
77.551
 
0.2%
48.901
 
0.2%
203.001
 
0.2%
42.641
 
0.2%
108.671
 
0.2%
Other values (88)88
 
17.6%
(Missing)398
79.6%

Length

2022-09-22T16:13:08.679751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
455.564
 
3.9%
65.462
 
2.0%
162.691
 
1.0%
340.841
 
1.0%
1,935.731
 
1.0%
2,925.751
 
1.0%
1,795.671
 
1.0%
480.851
 
1.0%
1,905.441
 
1.0%
1,622.511
 
1.0%
Other values (88)88
86.3%

Most occurring characters

ValueCountFrequency (%)
.102
17.6%
557
9.9%
254
9.3%
354
9.3%
452
9.0%
152
9.0%
646
8.0%
845
7.8%
740
 
6.9%
038
 
6.6%
Other values (2)38
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number469
81.1%
Other Punctuation109
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
557
12.2%
254
11.5%
354
11.5%
452
11.1%
152
11.1%
646
9.8%
845
9.6%
740
8.5%
038
8.1%
931
6.6%
Other Punctuation
ValueCountFrequency (%)
.102
93.6%
,7
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.102
17.6%
557
9.9%
254
9.3%
354
9.3%
452
9.0%
152
9.0%
646
8.0%
845
7.8%
740
 
6.9%
038
 
6.6%
Other values (2)38
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.102
17.6%
557
9.9%
254
9.3%
354
9.3%
452
9.0%
152
9.0%
646
8.0%
845
7.8%
740
 
6.9%
038
 
6.6%
Other values (2)38
 
6.6%

Power (kW)
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct172
Distinct (%)90.1%
Missing309
Missing (%)61.8%
Memory size4.0 KiB
600.00
 
3
3,296.00
 
2
792.00
 
2
240.00
 
2
1,498.90
 
2
Other values (167)
180 

Length

Max length9
Median length6
Mean length6.87434555
Min length5

Characters and Unicode

Total characters1313
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)80.6%

Sample

1st row21,100.00
2nd row29,899.23
3rd row2,942.13
4th row10,096.00
5th row7,438.28

Common Values

ValueCountFrequency (%)
600.003
 
0.6%
3,296.002
 
0.4%
792.002
 
0.4%
240.002
 
0.4%
1,498.902
 
0.4%
1,294.722
 
0.4%
740.002
 
0.4%
1,100.002
 
0.4%
1,436.002
 
0.4%
1,320.002
 
0.4%
Other values (162)170
34.0%
(Missing)309
61.8%

Length

2022-09-22T16:13:08.751447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
600.003
 
1.6%
1,353.952
 
1.0%
3,296.002
 
1.0%
1,134.002
 
1.0%
1,347.842
 
1.0%
260.002
 
1.0%
630.002
 
1.0%
1,897.022
 
1.0%
1,216.002
 
1.0%
13,620.002
 
1.0%
Other values (162)170
89.0%

Most occurring characters

ValueCountFrequency (%)
0312
23.8%
.191
14.5%
1119
 
9.1%
288
 
6.7%
586
 
6.5%
,84
 
6.4%
481
 
6.2%
378
 
5.9%
674
 
5.6%
967
 
5.1%
Other values (2)133
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1038
79.1%
Other Punctuation275
 
20.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0312
30.1%
1119
 
11.5%
288
 
8.5%
586
 
8.3%
481
 
7.8%
378
 
7.5%
674
 
7.1%
967
 
6.5%
867
 
6.5%
766
 
6.4%
Other Punctuation
ValueCountFrequency (%)
.191
69.5%
,84
30.5%

Most occurring scripts

ValueCountFrequency (%)
Common1313
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0312
23.8%
.191
14.5%
1119
 
9.1%
288
 
6.7%
586
 
6.5%
,84
 
6.4%
481
 
6.2%
378
 
5.9%
674
 
5.6%
967
 
5.1%
Other values (2)133
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1313
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0312
23.8%
.191
14.5%
1119
 
9.1%
288
 
6.7%
586
 
6.5%
,84
 
6.4%
481
 
6.2%
378
 
5.9%
674
 
5.6%
967
 
5.1%
Other values (2)133
10.1%

Power Source
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)0.5%
Missing309
Missing (%)61.8%
Memory size4.0 KiB
Submitted
191 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters1719
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSubmitted
2nd rowSubmitted
3rd rowSubmitted
4th rowSubmitted
5th rowSubmitted

Common Values

ValueCountFrequency (%)
Submitted191
38.2%
(Missing)309
61.8%

Length

2022-09-22T16:13:08.818525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:08.881986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
submitted191
100.0%

Most occurring characters

ValueCountFrequency (%)
t382
22.2%
S191
11.1%
u191
11.1%
b191
11.1%
m191
11.1%
i191
11.1%
e191
11.1%
d191
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1528
88.9%
Uppercase Letter191
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t382
25.0%
u191
12.5%
b191
12.5%
m191
12.5%
i191
12.5%
e191
12.5%
d191
12.5%
Uppercase Letter
ValueCountFrequency (%)
S191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1719
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t382
22.2%
S191
11.1%
u191
11.1%
b191
11.1%
m191
11.1%
i191
11.1%
e191
11.1%
d191
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t382
22.2%
S191
11.1%
u191
11.1%
b191
11.1%
m191
11.1%
i191
11.1%
e191
11.1%
d191
11.1%

Energy Efficiency [GFlops/Watts]
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct168
Distinct (%)88.0%
Missing309
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean8.58486911
Minimum0.19
Maximum62.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:08.944490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile1.345
Q12.89
median4.26
Q39.355
95-th percentile29.72
Maximum62.2
Range62.01
Interquartile range (IQR)6.465

Descriptive statistics

Standard deviation10.62291287
Coefficient of variation (CV)1.237399514
Kurtosis6.772993005
Mean8.58486911
Median Absolute Deviation (MAD)1.87
Skewness2.476402363
Sum1639.71
Variance112.8462777
MonotonicityNot monotonic
2022-09-22T16:13:09.028859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.085
 
1.0%
3.593
 
0.6%
1.633
 
0.6%
8.013
 
0.6%
1.742
 
0.4%
5.462
 
0.4%
2.392
 
0.4%
62
 
0.4%
0.642
 
0.4%
4.92
 
0.4%
Other values (158)165
33.0%
(Missing)309
61.8%
ValueCountFrequency (%)
0.192
0.4%
0.51
0.2%
0.591
0.2%
0.642
0.4%
0.651
0.2%
1.271
0.2%
1.321
0.2%
1.341
0.2%
1.351
0.2%
1.371
0.2%
ValueCountFrequency (%)
62.21
0.2%
52.231
0.2%
51.631
0.2%
50.031
0.2%
40.91
0.2%
40.731
0.2%
34.461
0.2%
31.541
0.2%
30.81
0.2%
29.921
0.2%

Memory
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing499
Missing (%)99.8%
Memory size4.0 KiB
513,792

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row513,792

Common Values

ValueCountFrequency (%)
513,7921
 
0.2%
(Missing)499
99.8%

Length

2022-09-22T16:13:09.102519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:09.161807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
513,7921
100.0%

Most occurring characters

ValueCountFrequency (%)
51
14.3%
11
14.3%
31
14.3%
,1
14.3%
71
14.3%
91
14.3%
21
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number6
85.7%
Other Punctuation1
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
51
16.7%
11
16.7%
31
16.7%
71
16.7%
91
16.7%
21
16.7%
Other Punctuation
ValueCountFrequency (%)
,1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
51
14.3%
11
14.3%
31
14.3%
,1
14.3%
71
14.3%
91
14.3%
21
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
14.3%
11
14.3%
31
14.3%
,1
14.3%
71
14.3%
91
14.3%
21
14.3%

Architecture
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Cluster
449 
MPP
51 

Length

Max length7
Median length7
Mean length6.592
Min length3

Characters and Unicode

Total characters3296
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMPP
2nd rowMPP
3rd rowMPP
4th rowCluster
5th rowCluster

Common Values

ValueCountFrequency (%)
Cluster449
89.8%
MPP51
 
10.2%

Length

2022-09-22T16:13:09.214086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:09.413681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
cluster449
89.8%
mpp51
 
10.2%

Most occurring characters

ValueCountFrequency (%)
C449
13.6%
l449
13.6%
u449
13.6%
s449
13.6%
t449
13.6%
e449
13.6%
r449
13.6%
P102
 
3.1%
M51
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2694
81.7%
Uppercase Letter602
 
18.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l449
16.7%
u449
16.7%
s449
16.7%
t449
16.7%
e449
16.7%
r449
16.7%
Uppercase Letter
ValueCountFrequency (%)
C449
74.6%
P102
 
16.9%
M51
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Latin3296
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C449
13.6%
l449
13.6%
u449
13.6%
s449
13.6%
t449
13.6%
e449
13.6%
r449
13.6%
P102
 
3.1%
M51
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C449
13.6%
l449
13.6%
u449
13.6%
s449
13.6%
t449
13.6%
e449
13.6%
r449
13.6%
P102
 
3.1%
M51
 
1.5%

Processor
Categorical

HIGH CARDINALITY

Distinct109
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Xeon Gold 6148 20C 2.4GHz
 
30
Xeon Gold 6252 24C 2.1GHz
 
29
AMD EPYC 7742 64C 2.25GHz
 
25
Xeon Gold 6133 20C 2.5GHz
 
21
Xeon E5-2673v4 20C 2.3GHz
 
19
Other values (104)
376 

Length

Max length42
Median length25
Mean length25.942
Min length16

Characters and Unicode

Total characters12971
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)8.4%

Sample

1st rowAMD Optimized 3rd Generation EPYC 64C 2GHz
2nd rowA64FX 48C 2.2GHz
3rd rowAMD Optimized 3rd Generation EPYC 64C 2GHz
4th rowIBM POWER9 22C 3.07GHz
5th rowIBM POWER9 22C 3.1GHz

Common Values

ValueCountFrequency (%)
Xeon Gold 6148 20C 2.4GHz30
 
6.0%
Xeon Gold 6252 24C 2.1GHz29
 
5.8%
AMD EPYC 7742 64C 2.25GHz25
 
5.0%
Xeon Gold 6133 20C 2.5GHz21
 
4.2%
Xeon E5-2673v4 20C 2.3GHz19
 
3.8%
Xeon Gold 6233 24C 2.5GHz19
 
3.8%
AMD EPYC 7763 64C 2.45GHz17
 
3.4%
Xeon Platinum 8280 28C 2.7GHz17
 
3.4%
Xeon Gold 6130 16C 2.1GHz16
 
3.2%
Xeon Gold 6248 20C 2.5GHz14
 
2.8%
Other values (99)293
58.6%

Length

2022-09-22T16:13:09.476388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xeon388
 
15.9%
gold216
 
8.9%
24c100
 
4.1%
20c97
 
4.0%
amd92
 
3.8%
epyc91
 
3.7%
platinum82
 
3.4%
2.1ghz77
 
3.2%
64c74
 
3.0%
2.5ghz69
 
2.8%
Other values (161)1152
47.3%

Most occurring characters

ValueCountFrequency (%)
1952
 
15.0%
21134
 
8.7%
G723
 
5.6%
o618
 
4.8%
C590
 
4.5%
6531
 
4.1%
n518
 
4.0%
H511
 
3.9%
z507
 
3.9%
4480
 
3.7%
Other values (46)5407
41.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number4079
31.4%
Lowercase Letter3312
25.5%
Uppercase Letter3093
23.8%
Space Separator1952
15.0%
Other Punctuation459
 
3.5%
Dash Punctuation76
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G723
23.4%
C590
19.1%
H511
16.5%
X395
12.8%
P189
 
6.1%
E180
 
5.8%
M105
 
3.4%
A100
 
3.2%
Y96
 
3.1%
D93
 
3.0%
Other values (12)111
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
o618
18.7%
n518
15.6%
z507
15.3%
e448
13.5%
l325
9.8%
d231
 
7.0%
t123
 
3.7%
i121
 
3.7%
a95
 
2.9%
m93
 
2.8%
Other values (11)233
 
7.0%
Decimal Number
ValueCountFrequency (%)
21134
27.8%
6531
13.0%
4480
11.8%
1433
 
10.6%
3339
 
8.3%
8330
 
8.1%
5297
 
7.3%
0249
 
6.1%
7224
 
5.5%
962
 
1.5%
Space Separator
ValueCountFrequency (%)
1952
100.0%
Other Punctuation
ValueCountFrequency (%)
.459
100.0%
Dash Punctuation
ValueCountFrequency (%)
-76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common6566
50.6%
Latin6405
49.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
G723
11.3%
o618
9.6%
C590
 
9.2%
n518
 
8.1%
H511
 
8.0%
z507
 
7.9%
e448
 
7.0%
X395
 
6.2%
l325
 
5.1%
d231
 
3.6%
Other values (33)1539
24.0%
Common
ValueCountFrequency (%)
1952
29.7%
21134
17.3%
6531
 
8.1%
4480
 
7.3%
.459
 
7.0%
1433
 
6.6%
3339
 
5.2%
8330
 
5.0%
5297
 
4.5%
0249
 
3.8%
Other values (3)362
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII12971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1952
 
15.0%
21134
 
8.7%
G723
 
5.6%
o618
 
4.8%
C590
 
4.5%
6531
 
4.1%
n518
 
4.0%
H511
 
3.9%
z507
 
3.9%
4480
 
3.7%
Other values (46)5407
41.7%

Processor Technology
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Intel Skylake
158 
Intel Cascade lake
132 
AMD Zen-2 (Rome)
57 
Intel Broadwell
49 
AMD Zen-3 (Milan)
34 
Other values (13)
70 

Length

Max length18
Median length17
Mean length15.088
Min length5

Characters and Unicode

Total characters7544
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.0%

Sample

1st rowAMD Zen-3 (Milan)
2nd rowFujitsu ARM
3rd rowAMD Zen-3 (Milan)
4th rowPower
5th rowPower

Common Values

ValueCountFrequency (%)
Intel Skylake158
31.6%
Intel Cascade lake132
26.4%
AMD Zen-2 (Rome)57
 
11.4%
Intel Broadwell49
 
9.8%
AMD Zen-3 (Milan)34
 
6.8%
Intel Haswell18
 
3.6%
Intel Ice Lake14
 
2.8%
Intel Xeon Phi8
 
1.6%
Intel IvyBridge7
 
1.4%
Power7
 
1.4%
Other values (8)16
 
3.2%

Length

2022-09-22T16:13:09.554517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
intel388
31.3%
skylake158
12.7%
lake146
 
11.8%
cascade132
 
10.6%
amd93
 
7.5%
zen-257
 
4.6%
rome57
 
4.6%
broadwell49
 
3.9%
zen-334
 
2.7%
milan34
 
2.7%
Other values (18)93
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e1093
14.5%
l849
11.3%
741
 
9.8%
a673
 
8.9%
n534
 
7.1%
k462
 
6.1%
I409
 
5.4%
t397
 
5.3%
d191
 
2.5%
y166
 
2.2%
Other values (40)2029
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5106
67.7%
Uppercase Letter1323
 
17.5%
Space Separator741
 
9.8%
Decimal Number96
 
1.3%
Open Punctuation93
 
1.2%
Close Punctuation93
 
1.2%
Dash Punctuation91
 
1.2%
Connector Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1093
21.4%
l849
16.6%
a673
13.2%
n534
10.5%
k462
9.0%
t397
 
7.8%
d191
 
3.7%
y166
 
3.3%
s157
 
3.1%
c150
 
2.9%
Other values (11)434
 
8.5%
Uppercase Letter
ValueCountFrequency (%)
I409
30.9%
S160
 
12.1%
C136
 
10.3%
M132
 
10.0%
A98
 
7.4%
Z93
 
7.0%
D93
 
7.0%
R62
 
4.7%
B57
 
4.3%
H18
 
1.4%
Other values (9)65
 
4.9%
Decimal Number
ValueCountFrequency (%)
258
60.4%
334
35.4%
62
 
2.1%
81
 
1.0%
41
 
1.0%
Space Separator
ValueCountFrequency (%)
741
100.0%
Open Punctuation
ValueCountFrequency (%)
(93
100.0%
Close Punctuation
ValueCountFrequency (%)
)93
100.0%
Dash Punctuation
ValueCountFrequency (%)
-91
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6429
85.2%
Common1115
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1093
17.0%
l849
13.2%
a673
10.5%
n534
 
8.3%
k462
 
7.2%
I409
 
6.4%
t397
 
6.2%
d191
 
3.0%
y166
 
2.6%
S160
 
2.5%
Other values (30)1495
23.3%
Common
ValueCountFrequency (%)
741
66.5%
(93
 
8.3%
)93
 
8.3%
-91
 
8.2%
258
 
5.2%
334
 
3.0%
62
 
0.2%
81
 
0.1%
_1
 
0.1%
41
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1093
14.5%
l849
11.3%
741
 
9.8%
a673
 
8.9%
n534
 
7.1%
k462
 
6.1%
I409
 
5.4%
t397
 
5.3%
d191
 
2.5%
y166
 
2.2%
Other values (40)2029
26.9%

Processor Speed (MHz)
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2,100
77 
2,500
69 
2,300
64 
2,400
52 
2,600
36 
Other values (23)
202 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)1.6%

Sample

1st row2,000
2nd row2,200
3rd row2,000
4th row3,070
5th row3,100

Common Values

ValueCountFrequency (%)
2,10077
15.4%
2,50069
13.8%
2,30064
12.8%
2,40052
10.4%
2,60036
7.2%
2,70035
7.0%
2,00030
 
6.0%
2,25025
 
5.0%
2,45022
 
4.4%
2,20021
 
4.2%
Other values (18)69
13.8%

Length

2022-09-22T16:13:09.621198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,10077
15.4%
2,50069
13.8%
2,30064
12.8%
2,40052
10.4%
2,60036
7.2%
2,70035
7.0%
2,00030
 
6.0%
2,25025
 
5.0%
2,45022
 
4.4%
2,20021
 
4.2%
Other values (18)69
13.8%

Most occurring characters

ValueCountFrequency (%)
0982
39.3%
2509
20.4%
,500
20.0%
5126
 
5.0%
398
 
3.9%
197
 
3.9%
486
 
3.4%
640
 
1.6%
736
 
1.4%
814
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2000
80.0%
Other Punctuation500
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0982
49.1%
2509
25.4%
5126
 
6.3%
398
 
4.9%
197
 
4.9%
486
 
4.3%
640
 
2.0%
736
 
1.8%
814
 
0.7%
912
 
0.6%
Other Punctuation
ValueCountFrequency (%)
,500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0982
39.3%
2509
20.4%
,500
20.0%
5126
 
5.0%
398
 
3.9%
197
 
3.9%
486
 
3.4%
640
 
1.6%
736
 
1.4%
814
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0982
39.3%
2509
20.4%
,500
20.0%
5126
 
5.0%
398
 
3.9%
197
 
3.9%
486
 
3.4%
640
 
1.6%
736
 
1.4%
814
 
0.6%

Operating System
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Linux
240 
CentOS
81 
Cray Linux Environment
25 
HPE Cray OS
 
15
Red Hat Enterprise Linux
 
12
Other values (45)
127 

Length

Max length35
Median length31
Mean length8.892
Min length4

Characters and Unicode

Total characters4446
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)3.6%

Sample

1st rowHPE Cray OS
2nd rowRed Hat Enterprise Linux
3rd rowHPE Cray OS
4th rowRHEL 7.4
5th rowRed Hat Enterprise Linux

Common Values

ValueCountFrequency (%)
Linux240
48.0%
CentOS81
 
16.2%
Cray Linux Environment 25
 
5.0%
HPE Cray OS15
 
3.0%
Red Hat Enterprise Linux12
 
2.4%
bullx SCS11
 
2.2%
Ubuntu 20.04.1 LTS7
 
1.4%
CentOS Linux 76
 
1.2%
SLES15 SP26
 
1.2%
Linux/TOSS6
 
1.2%
Other values (40)91
 
18.2%

Length

2022-09-22T16:13:09.688962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
linux310
38.7%
centos90
 
11.2%
cray40
 
5.0%
rhel31
 
3.9%
enterprise26
 
3.2%
environment25
 
3.1%
ubuntu24
 
3.0%
os17
 
2.1%
hpe15
 
1.9%
bullx13
 
1.6%
Other values (53)210
26.2%

Most occurring characters

ValueCountFrequency (%)
n545
12.3%
i389
 
8.7%
u382
 
8.6%
L373
 
8.4%
x333
 
7.5%
329
 
7.4%
S228
 
5.1%
e218
 
4.9%
t192
 
4.3%
r146
 
3.3%
Other values (46)1311
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2608
58.7%
Uppercase Letter1202
27.0%
Space Separator329
 
7.4%
Decimal Number226
 
5.1%
Other Punctuation77
 
1.7%
Dash Punctuation4
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n545
20.9%
i389
14.9%
u382
14.6%
x333
12.8%
e218
 
8.4%
t192
 
7.4%
r146
 
5.6%
a73
 
2.8%
y49
 
1.9%
b41
 
1.6%
Other values (14)240
9.2%
Uppercase Letter
ValueCountFrequency (%)
L373
31.0%
S228
19.0%
C142
 
11.8%
O122
 
10.1%
E120
 
10.0%
H58
 
4.8%
R51
 
4.2%
U36
 
3.0%
P29
 
2.4%
T21
 
1.7%
Other values (9)22
 
1.8%
Decimal Number
ValueCountFrequency (%)
146
20.4%
730
13.3%
029
12.8%
427
11.9%
225
11.1%
824
10.6%
518
 
8.0%
615
 
6.6%
312
 
5.3%
Other Punctuation
ValueCountFrequency (%)
.71
92.2%
/6
 
7.8%
Space Separator
ValueCountFrequency (%)
329
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3810
85.7%
Common636
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n545
14.3%
i389
10.2%
u382
10.0%
L373
9.8%
x333
 
8.7%
S228
 
6.0%
e218
 
5.7%
t192
 
5.0%
r146
 
3.8%
C142
 
3.7%
Other values (33)862
22.6%
Common
ValueCountFrequency (%)
329
51.7%
.71
 
11.2%
146
 
7.2%
730
 
4.7%
029
 
4.6%
427
 
4.2%
225
 
3.9%
824
 
3.8%
518
 
2.8%
615
 
2.4%
Other values (3)22
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n545
12.3%
i389
 
8.7%
u382
 
8.6%
L373
 
8.4%
x333
 
7.5%
329
 
7.4%
S228
 
5.1%
e218
 
4.9%
t192
 
4.3%
r146
 
3.3%
Other values (46)1311
29.5%

OS Family
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Linux
500 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLinux
2nd rowLinux
3rd rowLinux
4th rowLinux
5th rowLinux

Common Values

ValueCountFrequency (%)
Linux500
100.0%

Length

2022-09-22T16:13:09.757425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:09.814964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
linux500
100.0%

Most occurring characters

ValueCountFrequency (%)
L500
20.0%
i500
20.0%
n500
20.0%
u500
20.0%
x500
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2000
80.0%
Uppercase Letter500
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i500
25.0%
n500
25.0%
u500
25.0%
x500
25.0%
Uppercase Letter
ValueCountFrequency (%)
L500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L500
20.0%
i500
20.0%
n500
20.0%
u500
20.0%
x500
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L500
20.0%
i500
20.0%
n500
20.0%
u500
20.0%
x500
20.0%

Accelerator/Co-Processor
Categorical

HIGH CORRELATION

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
None
332 
NVIDIA Tesla V100
68 
NVIDIA A100
 
21
NVIDIA A100 SXM4 40 GB
 
12
NVIDIA Tesla V100 SXM2
 
11
Other values (21)
56 

Length

Max length37
Median length4
Mean length8.442
Min length4

Characters and Unicode

Total characters4221
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.8%

Sample

1st rowAMD Instinct MI250X
2nd rowNone
3rd rowAMD Instinct MI250X
4th rowNVIDIA Volta GV100
5th rowNVIDIA Volta GV100

Common Values

ValueCountFrequency (%)
None332
66.4%
NVIDIA Tesla V10068
 
13.6%
NVIDIA A10021
 
4.2%
NVIDIA A100 SXM4 40 GB12
 
2.4%
NVIDIA Tesla V100 SXM211
 
2.2%
NVIDIA A100 80GB​9
 
1.8%
NVIDIA A100 40GB8
 
1.6%
AMD Instinct MI250X7
 
1.4%
NVIDIA Tesla P1007
 
1.4%
NVIDIA Volta GV1004
 
0.8%
Other values (16)21
 
4.2%

Length

2022-09-22T16:13:09.868857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none332
38.6%
nvidia154
17.9%
tesla94
 
10.9%
v10080
 
9.3%
a10054
 
6.3%
sxm416
 
1.9%
gb16
 
1.9%
4012
 
1.4%
sxm211
 
1.3%
80gb​9
 
1.0%
Other values (31)81
 
9.4%

Most occurring characters

ValueCountFrequency (%)
N489
11.6%
e441
10.4%
359
 
8.5%
n354
 
8.4%
0347
 
8.2%
o345
 
8.2%
I323
 
7.7%
V244
 
5.8%
A215
 
5.1%
D162
 
3.8%
Other values (41)942
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1738
41.2%
Lowercase Letter1523
36.1%
Decimal Number588
 
13.9%
Space Separator359
 
8.5%
Format9
 
0.2%
Dash Punctuation3
 
0.1%
Other Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e441
29.0%
n354
23.2%
o345
22.7%
s104
 
6.8%
a102
 
6.7%
l102
 
6.7%
t25
 
1.6%
i15
 
1.0%
r8
 
0.5%
c8
 
0.5%
Other values (11)19
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
N489
28.1%
I323
18.6%
V244
14.0%
A215
12.4%
D162
 
9.3%
T94
 
5.4%
M44
 
2.5%
X37
 
2.1%
G37
 
2.1%
B33
 
1.9%
Other values (8)60
 
3.5%
Decimal Number
ValueCountFrequency (%)
0347
59.0%
1151
25.7%
441
 
7.0%
223
 
3.9%
814
 
2.4%
59
 
1.5%
32
 
0.3%
71
 
0.2%
Space Separator
ValueCountFrequency (%)
359
100.0%
Format
ValueCountFrequency (%)
9
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3
100.0%
Other Punctuation
ValueCountFrequency (%)
/1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3261
77.3%
Common960
 
22.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
N489
15.0%
e441
13.5%
n354
10.9%
o345
10.6%
I323
9.9%
V244
7.5%
A215
6.6%
D162
 
5.0%
s104
 
3.2%
a102
 
3.1%
Other values (29)482
14.8%
Common
ValueCountFrequency (%)
359
37.4%
0347
36.1%
1151
15.7%
441
 
4.3%
223
 
2.4%
814
 
1.5%
9
 
0.9%
59
 
0.9%
-3
 
0.3%
32
 
0.2%
Other values (2)2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4212
99.8%
Punctuation9
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N489
11.6%
e441
10.5%
359
 
8.5%
n354
 
8.4%
0347
 
8.2%
o345
 
8.2%
I323
 
7.7%
V244
 
5.8%
A215
 
5.1%
D162
 
3.8%
Other values (40)933
22.2%
Punctuation
ValueCountFrequency (%)
9
100.0%

Cores per Socket
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.868
Minimum4
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:09.931648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q118
median22
Q329
95-th percentile64
Maximum260
Range256
Interquartile range (IQR)11

Descriptive statistics

Standard deviation20.17415597
Coefficient of variation (CV)0.6988414844
Kurtosis33.61675403
Mean28.868
Median Absolute Deviation (MAD)6
Skewness3.78695318
Sum14434
Variance406.9965691
MonotonicityNot monotonic
2022-09-22T16:13:09.995178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
24100
20.0%
2097
19.4%
6474
14.8%
1644
8.8%
1841
8.2%
1227
 
5.4%
2821
 
4.2%
1418
 
3.6%
4816
 
3.2%
3214
 
2.8%
Other values (11)48
9.6%
ValueCountFrequency (%)
41
 
0.2%
61
 
0.2%
811
 
2.2%
106
 
1.2%
1227
 
5.4%
1418
 
3.6%
1644
8.8%
1841
8.2%
2097
19.4%
225
 
1.0%
ValueCountFrequency (%)
2601
 
0.2%
686
 
1.2%
6474
14.8%
4816
 
3.2%
402
 
0.4%
385
 
1.0%
367
 
1.4%
3214
 
2.8%
2821
 
4.2%
263
 
0.6%

Processor Generation
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Xeon Gold
126 
Xeon Gold 62xx (Cascade Lake)
91 
AMD Rome
57 
Intel Xeon E5 (Broadwell)
49 
Xeon Platinum 82xx (Cascade Lake)
35 
Other values (18)
142 

Length

Max length33
Median length30
Mean length17.898
Min length6

Characters and Unicode

Total characters8949
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.0%

Sample

1st rowAMD Milan
2nd rowFujitsu A64FX
3rd rowAMD Milan
4th rowIBM POWER9
5th rowIBM POWER9

Common Values

ValueCountFrequency (%)
Xeon Gold126
25.2%
Xeon Gold 62xx (Cascade Lake)91
18.2%
AMD Rome57
11.4%
Intel Xeon E5 (Broadwell)49
 
9.8%
Xeon Platinum 82xx (Cascade Lake)35
 
7.0%
AMD Milan34
 
6.8%
Xeon Platinum29
 
5.8%
Intel Xeon E5 (Haswell)16
 
3.2%
Xeon® Platinum 83xx (Ice Lake)14
 
2.8%
Intel Xeon Phi8
 
1.6%
Other values (13)41
 
8.2%

Length

2022-09-22T16:13:10.062707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xeon374
23.4%
gold217
13.6%
lake146
 
9.1%
cascade132
 
8.3%
amd93
 
5.8%
62xx91
 
5.7%
platinum84
 
5.3%
intel83
 
5.2%
e573
 
4.6%
rome57
 
3.6%
Other values (28)246
15.4%

Most occurring characters

ValueCountFrequency (%)
1096
 
12.2%
e915
 
10.2%
o716
 
8.0%
n602
 
6.7%
a602
 
6.7%
l559
 
6.2%
d408
 
4.6%
X394
 
4.4%
x294
 
3.3%
(222
 
2.5%
Other values (46)3141
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5302
59.2%
Uppercase Letter1700
 
19.0%
Space Separator1096
 
12.2%
Decimal Number389
 
4.3%
Open Punctuation222
 
2.5%
Close Punctuation222
 
2.5%
Other Symbol14
 
0.2%
Dash Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e915
17.3%
o716
13.5%
n602
11.4%
a602
11.4%
l559
10.5%
d408
7.7%
x294
 
5.5%
t177
 
3.3%
s160
 
3.0%
c150
 
2.8%
Other values (12)719
13.6%
Uppercase Letter
ValueCountFrequency (%)
X394
23.2%
G217
12.8%
L146
 
8.6%
M135
 
7.9%
C132
 
7.8%
I111
 
6.5%
P100
 
5.9%
A98
 
5.8%
D94
 
5.5%
E89
 
5.2%
Other values (10)184
10.8%
Decimal Number
ValueCountFrequency (%)
2133
34.2%
697
24.9%
574
19.0%
849
 
12.6%
314
 
3.6%
913
 
3.3%
45
 
1.3%
72
 
0.5%
02
 
0.5%
Space Separator
ValueCountFrequency (%)
1096
100.0%
Open Punctuation
ValueCountFrequency (%)
(222
100.0%
Close Punctuation
ValueCountFrequency (%)
)222
100.0%
Other Symbol
ValueCountFrequency (%)
®14
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7002
78.2%
Common1947
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e915
13.1%
o716
 
10.2%
n602
 
8.6%
a602
 
8.6%
l559
 
8.0%
d408
 
5.8%
X394
 
5.6%
x294
 
4.2%
G217
 
3.1%
t177
 
2.5%
Other values (32)2118
30.2%
Common
ValueCountFrequency (%)
1096
56.3%
(222
 
11.4%
)222
 
11.4%
2133
 
6.8%
697
 
5.0%
574
 
3.8%
849
 
2.5%
®14
 
0.7%
314
 
0.7%
913
 
0.7%
Other values (4)13
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8935
99.8%
None14
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1096
 
12.3%
e915
 
10.2%
o716
 
8.0%
n602
 
6.7%
a602
 
6.7%
l559
 
6.3%
d408
 
4.6%
X394
 
4.4%
x294
 
3.3%
(222
 
2.5%
Other values (45)3127
35.0%
None
ValueCountFrequency (%)
®14
100.0%

System Model
Categorical

HIGH CARDINALITY

Distinct131
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
ThinkSystem HR650X
43 
Sugon TC6000
 
33
Inspur NF5468M5
 
30
ThinkSystem C0366
 
26
BullSequana XH2000
 
22
Other values (126)
346 

Length

Max length39
Median length33
Mean length15.862
Min length9

Characters and Unicode

Total characters7931
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)14.2%

Sample

1st rowHPE Cray EX235a
2nd rowSupercomputer Fugaku
3rd rowHPE Cray EX235a
4th rowIBM Power System AC922
5th rowIBM Power System AC922

Common Values

ValueCountFrequency (%)
ThinkSystem HR650X43
 
8.6%
Sugon TC600033
 
6.6%
Inspur NF5468M530
 
6.0%
ThinkSystem C036626
 
5.2%
BullSequana XH200022
 
4.4%
Lenovo C104019
 
3.8%
ThinkSystem SR65016
 
3.2%
Cray XC4016
 
3.2%
ThinkSystem C239715
 
3.0%
HPE SGI 860011
 
2.2%
Other values (121)269
53.8%

Length

2022-09-22T16:13:10.135336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
thinksystem139
 
12.0%
cray56
 
4.8%
inspur51
 
4.4%
hr650x43
 
3.7%
sugon36
 
3.1%
hpe35
 
3.0%
tc600033
 
2.8%
bullsequana32
 
2.8%
nf5468m530
 
2.6%
c036626
 
2.2%
Other values (187)682
58.6%

Most occurring characters

ValueCountFrequency (%)
663
 
8.4%
0609
 
7.7%
S360
 
4.5%
e343
 
4.3%
n306
 
3.9%
5267
 
3.4%
C266
 
3.4%
6250
 
3.2%
s217
 
2.7%
T216
 
2.7%
Other values (58)4434
55.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3147
39.7%
Uppercase Letter2332
29.4%
Decimal Number1721
21.7%
Space Separator663
 
8.4%
Dash Punctuation51
 
0.6%
Other Punctuation13
 
0.2%
Connector Punctuation2
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e343
 
10.9%
n306
 
9.7%
s217
 
6.9%
u204
 
6.5%
y204
 
6.5%
r200
 
6.4%
t196
 
6.2%
o184
 
5.8%
i179
 
5.7%
l175
 
5.6%
Other values (16)939
29.8%
Uppercase Letter
ValueCountFrequency (%)
S360
15.4%
C266
11.4%
T216
 
9.3%
X192
 
8.2%
I139
 
6.0%
H132
 
5.7%
E125
 
5.4%
R124
 
5.3%
P115
 
4.9%
N96
 
4.1%
Other values (14)567
24.3%
Decimal Number
ValueCountFrequency (%)
0609
35.4%
5267
15.5%
6250
14.5%
2165
 
9.6%
4123
 
7.1%
187
 
5.1%
375
 
4.4%
873
 
4.2%
940
 
2.3%
732
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/10
76.9%
.2
 
15.4%
,1
 
7.7%
Space Separator
ValueCountFrequency (%)
663
100.0%
Dash Punctuation
ValueCountFrequency (%)
-51
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5479
69.1%
Common2452
30.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S360
 
6.6%
e343
 
6.3%
n306
 
5.6%
C266
 
4.9%
s217
 
4.0%
T216
 
3.9%
u204
 
3.7%
y204
 
3.7%
r200
 
3.7%
t196
 
3.6%
Other values (40)2967
54.2%
Common
ValueCountFrequency (%)
663
27.0%
0609
24.8%
5267
10.9%
6250
 
10.2%
2165
 
6.7%
4123
 
5.0%
187
 
3.5%
375
 
3.1%
873
 
3.0%
-51
 
2.1%
Other values (8)89
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII7931
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
663
 
8.4%
0609
 
7.7%
S360
 
4.5%
e343
 
4.3%
n306
 
3.9%
5267
 
3.4%
C266
 
3.4%
6250
 
3.2%
s217
 
2.7%
T216
 
2.7%
Other values (58)4434
55.9%

System Family
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Lenovo ThinkSystem
139 
Inspur Cluster
51 
Sugon Cluster
36 
BullSequana
32 
Cray XC
25 
Other values (46)
217 

Length

Max length30
Median length25
Mean length14.562
Min length7

Characters and Unicode

Total characters7281
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)4.4%

Sample

1st rowHPE Cray EX
2nd rowFujitsu Cluster
3rd rowHPE Cray EX
4th row IBM Power Systems
5th row IBM Power Systems

Common Values

ValueCountFrequency (%)
Lenovo ThinkSystem139
27.8%
Inspur Cluster51
 
10.2%
Sugon Cluster36
 
7.2%
BullSequana32
 
6.4%
Cray XC25
 
5.0%
HPE Cray EX23
 
4.6%
Lenovo Cluster21
 
4.2%
HPE Apollo21
 
4.2%
Dell PowerEdge Cluster20
 
4.0%
NVIDIA DGX16
 
3.2%
Other values (41)116
23.2%

Length

2022-09-22T16:13:10.210873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cluster182
17.4%
lenovo161
15.4%
thinksystem139
13.3%
cray56
 
5.4%
hpe56
 
5.4%
inspur51
 
4.9%
sugon36
 
3.4%
bullsequana32
 
3.1%
xc25
 
2.4%
ex23
 
2.2%
Other values (59)285
27.2%

Most occurring characters

ValueCountFrequency (%)
e630
 
8.7%
553
 
7.6%
o459
 
6.3%
n446
 
6.1%
s410
 
5.6%
u406
 
5.6%
t370
 
5.1%
l359
 
4.9%
r359
 
4.9%
C297
 
4.1%
Other values (46)2992
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4887
67.1%
Uppercase Letter1771
 
24.3%
Space Separator553
 
7.6%
Decimal Number58
 
0.8%
Dash Punctuation10
 
0.1%
Other Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e630
12.9%
o459
9.4%
n446
9.1%
s410
 
8.4%
u406
 
8.3%
t370
 
7.6%
l359
 
7.3%
r359
 
7.3%
y205
 
4.2%
i191
 
3.9%
Other values (16)1052
21.5%
Uppercase Letter
ValueCountFrequency (%)
C297
16.8%
S263
14.9%
L166
9.4%
T149
8.4%
E138
7.8%
I130
7.3%
P116
 
6.5%
X72
 
4.1%
H72
 
4.1%
A63
 
3.6%
Other values (12)305
17.2%
Decimal Number
ValueCountFrequency (%)
030
51.7%
612
 
20.7%
812
 
20.7%
22
 
3.4%
32
 
3.4%
Space Separator
ValueCountFrequency (%)
553
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%
Other Punctuation
ValueCountFrequency (%)
/2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6658
91.4%
Common623
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e630
 
9.5%
o459
 
6.9%
n446
 
6.7%
s410
 
6.2%
u406
 
6.1%
t370
 
5.6%
l359
 
5.4%
r359
 
5.4%
C297
 
4.5%
S263
 
4.0%
Other values (38)2659
39.9%
Common
ValueCountFrequency (%)
553
88.8%
030
 
4.8%
612
 
1.9%
812
 
1.9%
-10
 
1.6%
22
 
0.3%
/2
 
0.3%
32
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7281
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e630
 
8.7%
553
 
7.6%
o459
 
6.3%
n446
 
6.1%
s410
 
5.6%
u406
 
5.6%
t370
 
5.1%
l359
 
4.9%
r359
 
4.9%
C297
 
4.1%
Other values (46)2992
41.1%

Interconnect Family
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Gigabit Ethernet
228 
Infiniband
195 
Omnipath
39 
Custom Interconnect
32 
Proprietary Network
 
6

Length

Max length19
Median length16
Mean length13.264
Min length8

Characters and Unicode

Total characters6632
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGigabit Ethernet
2nd rowProprietary Network
3rd rowGigabit Ethernet
4th rowInfiniband
5th rowInfiniband

Common Values

ValueCountFrequency (%)
Gigabit Ethernet228
45.6%
Infiniband195
39.0%
Omnipath39
 
7.8%
Custom Interconnect32
 
6.4%
Proprietary Network6
 
1.2%

Length

2022-09-22T16:13:10.275408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:10.340633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
gigabit228
29.8%
ethernet228
29.8%
infiniband195
25.5%
omnipath39
 
5.1%
custom32
 
4.2%
interconnect32
 
4.2%
proprietary6
 
0.8%
network6
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n948
14.3%
i891
13.4%
t831
12.5%
e532
 
8.0%
a468
 
7.1%
b423
 
6.4%
r284
 
4.3%
h267
 
4.0%
266
 
4.0%
G228
 
3.4%
Other values (18)1494
22.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5600
84.4%
Uppercase Letter766
 
11.6%
Space Separator266
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n948
16.9%
i891
15.9%
t831
14.8%
e532
9.5%
a468
8.4%
b423
7.6%
r284
 
5.1%
h267
 
4.8%
g228
 
4.1%
d195
 
3.5%
Other values (10)533
9.5%
Uppercase Letter
ValueCountFrequency (%)
G228
29.8%
E228
29.8%
I227
29.6%
O39
 
5.1%
C32
 
4.2%
P6
 
0.8%
N6
 
0.8%
Space Separator
ValueCountFrequency (%)
266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6366
96.0%
Common266
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n948
14.9%
i891
14.0%
t831
13.1%
e532
8.4%
a468
 
7.4%
b423
 
6.6%
r284
 
4.5%
h267
 
4.2%
G228
 
3.6%
g228
 
3.6%
Other values (17)1266
19.9%
Common
ValueCountFrequency (%)
266
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n948
14.3%
i891
13.4%
t831
12.5%
e532
 
8.0%
a468
 
7.1%
b423
 
6.4%
r284
 
4.3%
h267
 
4.0%
266
 
4.0%
G228
 
3.4%
Other values (18)1494
22.5%

Interconnect
Categorical

HIGH CORRELATION

Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
25G Ethernet
69 
10G Ethernet
67 
100G Ethernet
48 
Infiniband EDR
41 
Intel Omni-Path
37 
Other values (33)
238 

Length

Max length58
Median length55
Mean length15.17
Min length6

Characters and Unicode

Total characters7585
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)2.8%

Sample

1st rowSlingshot-11
2nd rowTofu interconnect D
3rd rowSlingshot-11
4th rowDual-rail Mellanox EDR Infiniband
5th rowDual-rail Mellanox EDR Infiniband

Common Values

ValueCountFrequency (%)
25G Ethernet69
13.8%
10G Ethernet67
13.4%
100G Ethernet48
9.6%
Infiniband EDR41
 
8.2%
Intel Omni-Path37
 
7.4%
Infiniband HDR32
 
6.4%
Aries interconnect 25
 
5.0%
Mellanox HDR Infiniband24
 
4.8%
InfiniBand HDR10021
 
4.2%
Infiniband FDR20
 
4.0%
Other values (28)116
23.2%

Length

2022-09-22T16:13:10.409072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ethernet203
19.2%
infiniband190
18.0%
hdr73
 
6.9%
25g69
 
6.5%
10g67
 
6.3%
edr59
 
5.6%
100g48
 
4.5%
mellanox46
 
4.3%
omni-path39
 
3.7%
intel38
 
3.6%
Other values (38)226
21.4%

Most occurring characters

ValueCountFrequency (%)
n1034
 
13.6%
e600
 
7.9%
t593
 
7.8%
584
 
7.7%
i530
 
7.0%
a320
 
4.2%
r293
 
3.9%
0288
 
3.8%
E267
 
3.5%
h266
 
3.5%
Other values (42)2810
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4760
62.8%
Uppercase Letter1510
 
19.9%
Decimal Number649
 
8.6%
Space Separator584
 
7.7%
Dash Punctuation74
 
1.0%
Other Punctuation8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1034
21.7%
e600
12.6%
t593
12.5%
i530
11.1%
a320
 
6.7%
r293
 
6.2%
h266
 
5.6%
f200
 
4.2%
d197
 
4.1%
l180
 
3.8%
Other values (12)547
11.5%
Uppercase Letter
ValueCountFrequency (%)
E267
17.7%
I236
15.6%
G204
13.5%
D204
13.5%
R189
12.5%
H108
7.2%
B54
 
3.6%
P50
 
3.3%
M47
 
3.1%
O39
 
2.6%
Other values (9)112
7.4%
Decimal Number
ValueCountFrequency (%)
0288
44.4%
1183
28.2%
285
 
13.1%
569
 
10.6%
422
 
3.4%
61
 
0.2%
81
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/7
87.5%
.1
 
12.5%
Space Separator
ValueCountFrequency (%)
584
100.0%
Dash Punctuation
ValueCountFrequency (%)
-74
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6270
82.7%
Common1315
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1034
16.5%
e600
 
9.6%
t593
 
9.5%
i530
 
8.5%
a320
 
5.1%
r293
 
4.7%
E267
 
4.3%
h266
 
4.2%
I236
 
3.8%
G204
 
3.3%
Other values (31)1927
30.7%
Common
ValueCountFrequency (%)
584
44.4%
0288
21.9%
1183
 
13.9%
285
 
6.5%
-74
 
5.6%
569
 
5.2%
422
 
1.7%
/7
 
0.5%
61
 
0.1%
.1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1034
 
13.6%
e600
 
7.9%
t593
 
7.8%
584
 
7.7%
i530
 
7.0%
a320
 
4.2%
r293
 
3.9%
0288
 
3.8%
E267
 
3.5%
h266
 
3.5%
Other values (42)2810
37.0%

Continent
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Asia
228 
North America
142 
Europe
118 
South America
 
6
Oceania
 
5

Length

Max length13
Median length7
Mean length7.17
Min length4

Characters and Unicode

Total characters3585
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowNorth America
2nd rowAsia
3rd rowEurope
4th rowNorth America
5th rowNorth America

Common Values

ValueCountFrequency (%)
Asia228
45.6%
North America142
28.4%
Europe118
23.6%
South America6
 
1.2%
Oceania5
 
1.0%
Africa1
 
0.2%

Length

2022-09-22T16:13:10.475338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T16:13:10.543208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
asia228
35.2%
america148
22.8%
north142
21.9%
europe118
18.2%
south6
 
0.9%
oceania5
 
0.8%
africa1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r409
11.4%
a387
10.8%
i382
10.7%
A377
10.5%
e271
 
7.6%
o266
 
7.4%
s228
 
6.4%
c154
 
4.3%
148
 
4.1%
m148
 
4.1%
Other values (10)815
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2789
77.8%
Uppercase Letter648
 
18.1%
Space Separator148
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r409
14.7%
a387
13.9%
i382
13.7%
e271
9.7%
o266
9.5%
s228
8.2%
c154
 
5.5%
m148
 
5.3%
h148
 
5.3%
t148
 
5.3%
Other values (4)248
8.9%
Uppercase Letter
ValueCountFrequency (%)
A377
58.2%
N142
 
21.9%
E118
 
18.2%
S6
 
0.9%
O5
 
0.8%
Space Separator
ValueCountFrequency (%)
148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3437
95.9%
Common148
 
4.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r409
11.9%
a387
11.3%
i382
11.1%
A377
11.0%
e271
7.9%
o266
 
7.7%
s228
 
6.6%
c154
 
4.5%
m148
 
4.3%
h148
 
4.3%
Other values (9)667
19.4%
Common
ValueCountFrequency (%)
148
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3585
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r409
11.4%
a387
10.8%
i382
10.7%
A377
10.5%
e271
 
7.6%
o266
 
7.4%
s228
 
6.4%
c154
 
4.3%
148
 
4.1%
m148
 
4.1%
Other values (10)815
22.7%

Site ID
Categorical

HIGH CARDINALITY

Distinct233
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
50,329
 
37
50,905
 
26
50,854
 
21
50,171
 
14
50,787
 
12
Other values (228)
390 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)28.8%

Sample

1st row48,553
2nd row50,831
3rd row50,908
4th row48,553
5th row49,763

Common Values

ValueCountFrequency (%)
50,32937
 
7.4%
50,90526
 
5.2%
50,85421
 
4.2%
50,17114
 
2.8%
50,78712
 
2.4%
50,74311
 
2.2%
50,7748
 
1.6%
50,3307
 
1.4%
48,4486
 
1.2%
50,7596
 
1.2%
Other values (223)352
70.4%

Length

2022-09-22T16:13:10.604788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50,32937
 
7.4%
50,90526
 
5.2%
50,85421
 
4.2%
50,17114
 
2.8%
50,78712
 
2.4%
50,74311
 
2.2%
50,7748
 
1.6%
50,3307
 
1.4%
50,7596
 
1.2%
48,4486
 
1.2%
Other values (223)352
70.4%

Most occurring characters

ValueCountFrequency (%)
5519
17.3%
,500
16.7%
0483
16.1%
4292
9.7%
8244
8.1%
7231
7.7%
9225
7.5%
3161
 
5.4%
1129
 
4.3%
2122
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2500
83.3%
Other Punctuation500
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5519
20.8%
0483
19.3%
4292
11.7%
8244
9.8%
7231
9.2%
9225
9.0%
3161
 
6.4%
1129
 
5.2%
2122
 
4.9%
694
 
3.8%
Other Punctuation
ValueCountFrequency (%)
,500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5519
17.3%
,500
16.7%
0483
16.1%
4292
9.7%
8244
8.1%
7231
7.7%
9225
7.5%
3161
 
5.4%
1129
 
4.3%
2122
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5519
17.3%
,500
16.7%
0483
16.1%
4292
9.7%
8244
8.1%
7231
7.7%
9225
7.5%
3161
 
5.4%
1129
 
4.3%
2122
 
4.1%

System ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179678.018
Minimum176908
Maximum180085
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2022-09-22T16:13:10.672708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum176908
5-th percentile178604.05
Q1179581.75
median179813.5
Q3179953.25
95-th percentile180060.05
Maximum180085
Range3177
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation444.7186065
Coefficient of variation (CV)0.002475086332
Kurtosis8.909626906
Mean179678.018
Median Absolute Deviation (MAD)162
Skewness-2.563668451
Sum89839009
Variance197774.639
MonotonicityNot monotonic
2022-09-22T16:13:10.753212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800471
 
0.2%
1796521
 
0.2%
1798241
 
0.2%
1798231
 
0.2%
1798401
 
0.2%
1800191
 
0.2%
1800761
 
0.2%
1800271
 
0.2%
1797551
 
0.2%
1798441
 
0.2%
Other values (490)490
98.0%
ValueCountFrequency (%)
1769081
0.2%
1769291
0.2%
1772591
0.2%
1778241
0.2%
1779991
0.2%
1780711
0.2%
1782501
0.2%
1784251
0.2%
1784311
0.2%
1784321
0.2%
ValueCountFrequency (%)
1800851
0.2%
1800841
0.2%
1800831
0.2%
1800821
0.2%
1800811
0.2%
1800801
0.2%
1800791
0.2%
1800781
0.2%
1800771
0.2%
1800761
0.2%

Interactions

2022-09-22T16:13:04.860623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.602727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.167532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.715989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.457368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.977462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.548322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.104434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.946231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.697253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.232644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.788192image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.519889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.046011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.617619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.174266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.025591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.760571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.294441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.856677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.579576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.114250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.689326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.240079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.107537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.827609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.356302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.943160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.645351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.187187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.753366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.310268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.181351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.889966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.419983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.021953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.709686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.260630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.825227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.379421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.263841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:00.957946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.490495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.100800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.780212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.335362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.894360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.455551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.333004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.024187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.563748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.170051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.845499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.403372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.966004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.534003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:05.406110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.096395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:01.637238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.241304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:02.908832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:03.473930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.035254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-22T16:13:04.616220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-22T16:13:10.824546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T16:13:10.919966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T16:13:11.014356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T16:13:11.263006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T16:13:05.582944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T16:13:06.207454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T16:13:06.395944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T16:13:06.520934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

RankPrevious RankFirst AppearanceFirst RankNameComputerSiteManufacturerCountryYearSegmentTotal CoresAccelerator/Co-Processor CoresRmax [TFlop/s]Rpeak [TFlop/s]NmaxNhalfHPCG [TFlop/s]Power (kW)Power SourceEnergy Efficiency [GFlops/Watts]MemoryArchitectureProcessorProcessor TechnologyProcessor Speed (MHz)Operating SystemOS FamilyAccelerator/Co-ProcessorCores per SocketProcessor GenerationSystem ModelSystem FamilyInterconnect FamilyInterconnectContinentSite IDSystem ID
01NaN591FrontierHPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-11DOE/SC/Oak Ridge National LaboratoryHPEUnited States2021Research8,730,1128,138,2401,102,000.001,685,651.4624,440,832NaNNaN21,100.00Submitted52.23NaNMPPAMD Optimized 3rd Generation EPYC 64C 2GHzAMD Zen-3 (Milan)2,000HPE Cray OSLinuxAMD Instinct MI250X64AMD MilanHPE Cray EX235aHPE Cray EXGigabit EthernetSlingshot-11North America48,553180047
121.0551Supercomputer FugakuSupercomputer Fugaku, A64FX 48C 2.2GHz, Tofu interconnect DRIKEN Center for Computational ScienceFujitsuJapan2020Research7,630,848NaN442,010.00537,212.0021,288,960NaN16,004.5029,899.23Submitted14.78NaNMPPA64FX 48C 2.2GHzFujitsu ARM2,200Red Hat Enterprise LinuxLinuxNone48Fujitsu A64FXSupercomputer FugakuFujitsu ClusterProprietary NetworkTofu interconnect DAsia50,831179807
23NaN593LUMIHPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-11EuroHPC/CSCHPEFinland2022Research1,110,1441,034,880151,900.00214,351.878,709,120NaN1,935.732,942.13Submitted51.63NaNMPPAMD Optimized 3rd Generation EPYC 64C 2GHzAMD Zen-3 (Milan)2,000HPE Cray OSLinuxAMD Instinct MI250X64AMD MilanHPE Cray EX235aHPE Cray EXGigabit EthernetSlingshot-11Europe50,908180048
342.0511SummitIBM Power System AC922, IBM POWER9 22C 3.07GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR InfinibandDOE/SC/Oak Ridge National LaboratoryIBMUnited States2018Research2,414,5922,211,840148,600.00200,794.8816,473,600NaN2,925.7510,096.00Submitted14.72NaNClusterIBM POWER9 22C 3.07GHzPower3,070RHEL 7.4LinuxNVIDIA Volta GV10022IBM POWER9IBM Power System AC922IBM Power SystemsInfinibandDual-rail Mellanox EDR InfinibandNorth America48,553179397
453.0513SierraIBM Power System AC922, IBM POWER9 22C 3.1GHz, NVIDIA Volta GV100, Dual-rail Mellanox EDR InfinibandDOE/NNSA/LLNLIBM / NVIDIA / MellanoxUnited States2018Research1,572,4801,382,40094,640.00125,712.0011,902,464NaN1,795.677,438.28Submitted12.72NaNClusterIBM POWER9 22C 3.1GHzPower3,100Red Hat Enterprise LinuxLinuxNVIDIA Volta GV10022IBM POWER9IBM Power System AC922IBM Power SystemsInfinibandDual-rail Mellanox EDR InfinibandNorth America49,763179398
564.0471Sunway TaihuLightSunway MPP, Sunway SW26010 260C 1.45GHz, SunwayNational Supercomputing Center in WuxiNRCPCChina2016Research10,649,600NaN93,014.59125,435.9012,288,000NaN480.8515,371.00Submitted6.05NaNMPPSunway SW26010 260C 1.45GHzShenWei1,450Sunway RaiseOS 2.0.5LinuxNone260SunwaySunway MPPSunway ClusterCustom InterconnectSunwayAsia50,623178764
675.0575PerlmutterHPE Cray EX235n, AMD EPYC 7763 64C 2.45GHz, NVIDIA A100 SXM4 40 GB, Slingshot-10DOE/SC/LBNL/NERSCHPEUnited States2021Research761,856663,55270,870.0093,750.005,566,464NaN1,905.442,589.00Submitted27.37NaNMPPAMD EPYC 7763 64C 2.45GHzAMD Zen-3 (Milan)2,450HPE Cray OSLinuxNVIDIA A100 SXM4 40 GB64AMD MilanHPE Cray EX235nHPE Cray EXGigabit EthernetSlingshot-10North America48,429179972
786.0557SeleneNVIDIA DGX A100, AMD EPYC 7742 64C 2.25GHz, NVIDIA A100, Mellanox HDR InfinibandNVIDIA CorporationNvidiaUnited States2020Vendor555,520483,84063,460.0079,215.006,598,656NaN1,622.512,646.00Submitted23.98NaNClusterAMD EPYC 7742 64C 2.25GHzAMD Zen-2 (Rome)2,250Ubuntu 20.04.1 LTSLinuxNVIDIA A10064AMD RomeNVIDIA DGX A100NVIDIA DGXInfinibandMellanox HDR InfinibandNorth America48,448179842
897.0411Tianhe-2ATH-IVB-FEP Cluster, Intel Xeon E5-2692v2 12C 2.2GHz, TH Express-2, Matrix-2000National Super Computer Center in GuangzhouNUDTChina2018Research4,981,7604,554,75261,444.50100,678.669,773,000NaNNaN18,482.00Submitted3.32NaNClusterIntel Xeon E5-2692v2 12C 2.2GHzIntel IvyBridge2,200Kylin LinuxLinuxMatrix-200012Intel Xeon E5 (IvyBridge)TH-IVB-FEP ClusterTH-IVB ClusterCustom InterconnectTH Express-2Asia50,365177999
910NaN5910AdastraHPE Cray EX235a, AMD Optimized 3rd Generation EPYC 64C 2GHz, AMD Instinct MI250X, Slingshot-11Grand Equipement National de Calcul Intensif - Centre Informatique National de l'Enseignement Suprieur (GENCI-CINES)HPEFrance2022Academic319,072297,44046,100.0061,607.944,492,800NaN562.01921.48Submitted50.03NaNMPPAMD Optimized 3rd Generation EPYC 64C 2GHzAMD Zen-3 (Milan)2,000HPE Cray OSLinuxAMD Instinct MI250X64AMD MilanHPE Cray EX235aHPE Cray EXGigabit EthernetSlingshot-11Europe50,203180051

Last rows

RankPrevious RankFirst AppearanceFirst RankNameComputerSiteManufacturerCountryYearSegmentTotal CoresAccelerator/Co-Processor CoresRmax [TFlop/s]Rpeak [TFlop/s]NmaxNhalfHPCG [TFlop/s]Power (kW)Power SourceEnergy Efficiency [GFlops/Watts]MemoryArchitectureProcessorProcessor TechnologyProcessor Speed (MHz)Operating SystemOS FamilyAccelerator/Co-ProcessorCores per SocketProcessor GenerationSystem ModelSystem FamilyInterconnect FamilyInterconnectContinentSite IDSystem ID
490491456.051110Software Company (M) A11Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179341
491492457.051111Software Company (M) A10Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179342
492493458.051112Software Company (M) A9Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179343
493494459.051113Software Company (M) A8Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179344
494495460.051114Software Company (M) A7Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179345
495496461.051115Software Company (M) A6Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179346
496497462.051116Software Company (M) A4Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179347
497498463.051117Software Company (M) A3Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2018Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179348
498499464.051118Software Company (M) A2Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2017Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179349
499500465.051119Software Company (M) A1Lenovo C1040, Xeon E5-2673v4 20C 2.3GHz, 40G EthernetHosting ServicesLenovoUnited States2017Industry57,600NaN1,649.112,119.688,586,432NaNNaNNaNNaNNaNNaNClusterXeon E5-2673v4 20C 2.3GHzIntel Broadwell2,300LinuxLinuxNone20Intel Xeon E5 (Broadwell)Lenovo C1040Lenovo ClusterGigabit Ethernet40G EthernetNorth America50,171179350